Publications

2021 [ nach oben ]

Sizeless: Predicting the Optimal Size of Serverless Functions. S. Eismann; L. Bui; J. Grohmann; C. Abad; N. Herbst; S. Kounev; in Proceedings of the 22nd International MIDDLEWARE Conference (2021).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it predicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average prediction error of 15.3%. Based on these predictions, it selects the optimal memory size for 79.0% of the serverless functions and the secondbest memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.
@inproceedings{eismann2021sizeless, abstract = {Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it predicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average prediction error of 15.3%. Based on these predictions, it selects the optimal memory size for 79.0% of the serverless functions and the secondbest memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.}, author = {Eismann, Simon and Bui, Long and Grohmann, Johannes and Abad, Cristina and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 22nd International MIDDLEWARE Conference}, keywords = {t_full descartes}, title = {Sizeless: Predicting the Optimal Size of Serverless Functions}, year = 2021 }
%0 Conference Paper %1 eismann2021sizeless %A Eismann, Simon %A Bui, Long %A Grohmann, Johannes %A Abad, Cristina %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 22nd International MIDDLEWARE Conference %D 2021 %T Sizeless: Predicting the Optimal Size of Serverless Functions %X Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it predicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average prediction error of 15.3%. Based on these predictions, it selects the optimal memory size for 79.0% of the serverless functions and the secondbest memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.

SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation. J. Grohmann; S. Eismann; A. Bauer; S. Spinner; J. Blum; N. Herbst; S. Kounev; in ACM Transactions on Autonomous and Adaptive Systems (2021). 15(2)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.
@article{GrEiBaSpBlHeKo2021-TaaS-SARDE, abstract = {Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.}, address = {New York, NY, USA}, author = {Grohmann, Johannes and Eismann, Simon and Bauer, Andr{é} and Spinner, Simon and Blum, Johannes and Herbst, Nikolas and Kounev, Samuel}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {descartes algorithms resource Self-adaptive learning sys:relevantfor:se-group demand performance self-tuning estimation systems optimization statistical_estimation_and_machine_learning machine prediction t_journalmagazine}, month = {jun}, number = 2, publisher = {Association for Computing Machinery}, title = {SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation}, volume = 15, year = 2021 }
%0 Journal Article %1 GrEiBaSpBlHeKo2021-TaaS-SARDE %A Grohmann, Johannes %A Eismann, Simon %A Bauer, André %A Spinner, Simon %A Blum, Johannes %A Herbst, Nikolas %A Kounev, Samuel %C New York, NY, USA %D 2021 %I Association for Computing Machinery %J ACM Transactions on Autonomous and Adaptive Systems %N 2 %T SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation %U https://doi.org/10.1145/3463369 %V 15 %X Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.

Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies. V. Lesch; T. Noack; J. Hefter; S. Kounev; C. Krupitzer; in Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021 (2021).
Best Paper Award Runner Up, Full Paper Acceptance Ratio: 25%
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@inproceedings{lesch2021towards, author = {Lesch, Veronika and Noack, Tanja and Hefter, Johannes and Kounev, Samuel and Krupitzer, Christian}, booktitle = {Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021}, keywords = {t_full descartes cyber-physical_systems internet_of_things}, note = {Best Paper Award Runner Up, Full Paper Acceptance Ratio: 25%}, publisher = {IEEE}, title = {Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies}, year = 2021 }
%0 Conference Paper %1 lesch2021towards %A Lesch, Veronika %A Noack, Tanja %A Hefter, Johannes %A Kounev, Samuel %A Krupitzer, Christian %B Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021 %D 2021 %I IEEE %T Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies

A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools. M. Züfle; F. Moog; V. Lesch; C. Krupitzer; S. Kounev; in ISA Transactions: The Journal of Automation (2021).
(in press)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]
 
Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.
@article{zuefle2021anomalydetection, abstract = {Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.}, author = {Züfle, Marwin and Moog, Felix and Lesch, Veronika and Krupitzer, Christian and Kounev, Samuel}, journal = {ISA Transactions: The Journal of Automation}, keywords = {Cyber-Physical_Systems descartes statistical_estimation_and_machine_learning Industry_4_0 prediction t_interdisciplinary t_journalmagazine}, note = {(in press)}, title = {A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools}, year = 2021 }
%0 Journal Article %1 zuefle2021anomalydetection %A Züfle, Marwin %A Moog, Felix %A Lesch, Veronika %A Krupitzer, Christian %A Kounev, Samuel %D 2021 %J ISA Transactions: The Journal of Automation %T A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools %U https://www.sciencedirect.com/science/article/pii/S0019057821003773 %X Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

Performance Evaluation of a Post-Quantum Public-Key Cryptosystem. T. Prantl; D. Prantl; L. Beierlieb; L. Iffländer; A. Dmitrienko; C. Krupitzer; S. Kounev; in 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{PrPrBeIfDnKrKo2021-IPCCC-QuantumPublicKeyCryposystem, author = {Prantl, Thomas and Prantl, Dominik and Beierlieb, Lukas and Iffländer, Lukas and Dmitrienko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, booktitle = {2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC)}, keywords = {t_full descartes sss-group sys:relevantfor:se-group t_workshop}, organization = {IEEE}, title = {Performance Evaluation of a Post-Quantum Public-Key Cryptosystem}, year = 2021 }
%0 Conference Paper %1 PrPrBeIfDnKrKo2021-IPCCC-QuantumPublicKeyCryposystem %A Prantl, Thomas %A Prantl, Dominik %A Beierlieb, Lukas %A Iffländer, Lukas %A Dmitrienko, Alexandra %A Krupitzer, Christian %A Kounev, Samuel %B 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) %D 2021 %T Performance Evaluation of a Post-Quantum Public-Key Cryptosystem

Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT. T. Prantl; D. Prantl; A. Bauer; L. Iffländer; A. Dmitrenko; C. Krupitzer; S. Kounev; in 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{PrPrBaIfDmKrKo2021-IPCCC-QuantumGroupEncryption, author = {Prantl, Thomas and Prantl, Dominik and Bauer, André and Iffländer, Lukas and Dmitrenko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, booktitle = {2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC)}, keywords = {t_full descartes sss-group}, title = {Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT}, year = 2021 }
%0 Conference Paper %1 PrPrBaIfDmKrKo2021-IPCCC-QuantumGroupEncryption %A Prantl, Thomas %A Prantl, Dominik %A Bauer, André %A Iffländer, Lukas %A Dmitrenko, Alexandra %A Krupitzer, Christian %A Kounev, Samuel %B 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) %D 2021 %T Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT

The integration of training and off-training activities substantially alters training volume and load analysis in elite rowers. G. Treff; R. Leppich; K. Winkert; J. M. Steinacker; B. Mayer; B. Sperlich; in Scientific reports (2021). 11(1) 1–10.
[ BibTeX ] [ EndNote ]
 
@article{treff2021integration, author = {Treff, Gunnar and Leppich, Robert and Winkert, Kay and Steinacker, J{ü}rgen M and Mayer, Benjamin and Sperlich, Billy}, journal = {Scientific reports}, keywords = {descartes t_interdisciplinary t_journalmagazine}, number = 1, pages = {1–10}, publisher = {Nature Publishing Group}, title = {The integration of training and off-training activities substantially alters training volume and load analysis in elite rowers}, volume = 11, year = 2021 }
%0 Journal Article %1 treff2021integration %A Treff, Gunnar %A Leppich, Robert %A Winkert, Kay %A Steinacker, Jürgen M %A Mayer, Benjamin %A Sperlich, Billy %D 2021 %I Nature Publishing Group %J Scientific reports %N 1 %P 1--10 %T The integration of training and off-training activities substantially alters training volume and load analysis in elite rowers %V 11

The State of Serverless Applications: Collection, Characterization, and Community Consensus. S. Eismann; J. Scheuner; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; in Transactions on Software Engineering (2021).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.
@article{eismann2021state, abstract = {Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.}, author = {Eismann, Simon and Scheuner, Joel and van Eyk, Erwin and Schwinger, Maximilian and Grohmann, Johannes and Herbst, Nikolas and Abad, Cristina and Iosup, Alexandru}, journal = {Transactions on Software Engineering}, keywords = {descartes t_journalmagazine}, title = {The State of Serverless Applications: Collection, Characterization, and Community Consensus}, year = 2021 }
%0 Journal Article %1 eismann2021state %A Eismann, Simon %A Scheuner, Joel %A van Eyk, Erwin %A Schwinger, Maximilian %A Grohmann, Johannes %A Herbst, Nikolas %A Abad, Cristina %A Iosup, Alexandru %D 2021 %J Transactions on Software Engineering %T The State of Serverless Applications: Collection, Characterization, and Community Consensus %U https://ieeexplore.ieee.org/document/9543531/ %X Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.

Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping. N. Finke; M. Mohr; A. Lontke; M. Züfle; S. Kounev; R. Möller; in Research Challenges in Information Science (RCIS) (2021). 189–204.
[ BibTeX ] [ EndNote ]
 
@inproceedings{finke2021recommendations, author = {Finke, Nils and Mohr, Marisa and Lontke, Alexander and Z{ü}fle, Marwin and Kounev, Samuel and M{ö}ller, Ralf}, booktitle = {Research Challenges in Information Science (RCIS)}, keywords = {t_full descartes statistical_estimation_and_machine_learning prediction t_interdisciplinary}, organization = {Springer}, pages = {189–204}, title = {Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping}, year = 2021 }
%0 Conference Paper %1 finke2021recommendations %A Finke, Nils %A Mohr, Marisa %A Lontke, Alexander %A Züfle, Marwin %A Kounev, Samuel %A Möller, Ralf %B Research Challenges in Information Science (RCIS) %D 2021 %P 189--204 %T Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping

Serverless Computing (Dagstuhl Seminar 21201). C. Abad; I. T. Foster; N. Herbst; A. Iosup; in Dagstuhl Reports, (C. Abad; I. T. Foster; N. Herbst; A. Iosup, Hrsg.) (2021). 11(4) 34–93.
[ BibTeX ] [ EndNote ] [ URL ]
 
@article{abad_et_al:DagRep.11.4.34, address = {Dagstuhl, Germany}, annote = {Keywords: Cloud computing, Cloud continuum, data-driven, design patterns, DevOps, experimentation, model-driven, serverless computing, simulation, software architecture, systems management, vision}, author = {Abad, Cristina and Foster, Ian T. and Herbst, Nikolas and Iosup, Alexandru}, editor = {Abad, Cristina and Foster, Ian T. and Herbst, Nikolas and Iosup, Alexandru}, journal = {Dagstuhl Reports}, keywords = {cloud descartes metrics_and_benchmarking_methodologies elasticity sys:relevantfor:se-group resource_management}, number = 4, pages = {34–93}, publisher = {Schloss Dagstuhl – Leibniz-Zentrum f{ü}r Informatik}, title = {{Serverless Computing (Dagstuhl Seminar 21201)}}, volume = 11, year = 2021 }
%0 Journal Article %1 abad_et_al:DagRep.11.4.34 %A Abad, Cristina %A Foster, Ian T. %A Herbst, Nikolas %A Iosup, Alexandru %C Dagstuhl, Germany %D 2021 %E Abad, Cristina %E Foster, Ian T. %E Herbst, Nikolas %E Iosup, Alexandru %I Schloss Dagstuhl -- Leibniz-Zentrum für Informatik %J Dagstuhl Reports %N 4 %P 34--93 %T Serverless Computing (Dagstuhl Seminar 21201) %U https://drops.dagstuhl.de/opus/volltexte/2021/14798 %V 11 %Z Keywords: Cloud computing, Cloud continuum, data-driven, design patterns, DevOps, experimentation, model-driven, serverless computing, simulation, software architecture, systems management, vision

Establishing a cardiac training group for patients with heart failure: the “HIP-in-Würzburg” study. G. Güder; J. Wilkesmann; N. Scholz; R. Leppich; P. Düking; B. Sperlich; C. Rost; S. Frantz; C. Morbach; F. Sahiti; others; in Clinical Research in Cardiology (2021). 1–10.
[ BibTeX ] [ EndNote ]
 
@article{guder2021establishing, author = {G{ü}der, G{ü}lmisal and Wilkesmann, Joana and Scholz, Nina and Leppich, Robert and D{ü}king, Peter and Sperlich, Billy and Rost, Christian and Frantz, Stefan and Morbach, Caroline and Sahiti, Floran and others}, journal = {Clinical Research in Cardiology}, keywords = {descartes medicine t_journalmagazine}, pages = {1–10}, publisher = {Springer}, title = {Establishing a cardiac training group for patients with heart failure: the “HIP-in-W{ü}rzburg” study}, year = 2021 }
%0 Journal Article %1 guder2021establishing %A Güder, Gülmisal %A Wilkesmann, Joana %A Scholz, Nina %A Leppich, Robert %A Düking, Peter %A Sperlich, Billy %A Rost, Christian %A Frantz, Stefan %A Morbach, Caroline %A Sahiti, Floran %A others, %D 2021 %I Springer %J Clinical Research in Cardiology %P 1--10 %T Establishing a cardiac training group for patients with heart failure: the “HIP-in-Würzburg” study

Utility-based Vehicle Routing Integrating User Preferences. V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; in Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021 (2021).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{lesch2021utility, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, booktitle = {Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021}, keywords = {descartes intelligent_transportation_systems cyber-physical_systems internet_of_things t_workshop}, month = {March}, publisher = {IEEE}, title = {Utility-based Vehicle Routing Integrating User Preferences}, year = 2021 }
%0 Conference Paper %1 lesch2021utility %A Lesch, Veronika %A Hadry, Marius %A Kounev, Samuel %A Krupitzer, Christian %B Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021 %D 2021 %I IEEE %T Utility-based Vehicle Routing Integrating User Preferences

Auswirkungen der COVID-19-Pandemie auf Sportlerinnen und Sportler in Deutschland. C. Zinner; M. Matzka; R. Leppich; S. Kounev; H.-C. Holmberg; B. Sperlich; in 53. asp Jahrestagung (2021).
[ BibTeX ] [ EndNote ]
 
@article{zinner2021auswirkungen, author = {Zinner, Christoph and Matzka, Manuel and Leppich, Robert and Kounev, Samuel and Holmberg, Hans-Christian and Sperlich, Billy}, journal = {53. asp Jahrestagung}, keywords = {t_extendedabstract descartes t_interdisciplinary}, title = {Auswirkungen der COVID-19-Pandemie auf Sportlerinnen und Sportler in Deutschland}, year = 2021 }
%0 Journal Article %1 zinner2021auswirkungen %A Zinner, Christoph %A Matzka, Manuel %A Leppich, Robert %A Kounev, Samuel %A Holmberg, Hans-Christian %A Sperlich, Billy %D 2021 %J 53. asp Jahrestagung %T Auswirkungen der COVID-19-Pandemie auf Sportlerinnen und Sportler in Deutschland

A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0. M. Züfle; J. Agne; J. Grohmann; I. Dörtoluk; S. Kounev; in Proceedings of the 21st IEEE IES International Conference on Industrial Informatics (2021).
(to appear)
[ BibTeX ] [ EndNote ]
 
@inproceedings{ZuAgGrDoKo-INDIN2021-PdM, author = {Züfle, Marwin and Agne, Joachim and Grohmann, Johannes and Dörtoluk, Ibrahim and Kounev, Samuel}, booktitle = {Proceedings of the 21st IEEE IES International Conference on Industrial Informatics}, keywords = {Internet_of_Things Cyber-Physical_Systems t_full descartes statistical_estimation_and_machine_learning prediction t_interdisciplinary}, month = {July}, note = {(to appear)}, publisher = {IEEE}, series = {INDIN'21}, title = {A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0}, year = 2021 }
%0 Conference Paper %1 ZuAgGrDoKo-INDIN2021-PdM %A Züfle, Marwin %A Agne, Joachim %A Grohmann, Johannes %A Dörtoluk, Ibrahim %A Kounev, Samuel %B Proceedings of the 21st IEEE IES International Conference on Industrial Informatics %D 2021 %I IEEE %T A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0

SPEC Research Group Newsletter, vol. 3 no. 2. S. Kounev; A. van Hoorn; A. Bauer; (2021, April).
Published by Standard Performance Evaluation Corporation (SPEC)
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@misc{SPEC-RG-NEWSLETTER-10, author = {Kounev, Samuel and van Hoorn, Andre and Bauer, Andr{é}}, keywords = {t_other descartes SPEC}, month = {April}, note = {Published by Standard Performance Evaluation Corporation (SPEC)}, title = {{SPEC Research Group Newsletter, vol. 3 no. 2}}, year = 2021 }
%0 Generic %1 SPEC-RG-NEWSLETTER-10 %A Kounev, Samuel %A van Hoorn, Andre %A Bauer, André %D 2021 %T SPEC Research Group Newsletter, vol. 3 no. 2 %U http://research.spec.org/en/newsletter.html

Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads. J. Domaschka; S. Eismann; M. Leznik; J. Grohmann; S. Kounev; D. Seybold; in Companion of the ACM/SPEC International Conference on Performance Engineering (2021). (Bd. ICPE ’21) 175–178.
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Distributed Database Management Systems (DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance.We propose collecting real-world traces and then applying data generation techniques to synthesize similar realistic traces based on it. Based in this approach, we can obtain workloads for benchmarking, exhibit variability with respect to different aspects of interest while still being similar to the original traces. Varying generation parameters, we are able to support benchmarking what-if scenarios with hypothetical workloads and introduced anomalies.
@inproceedings{domaschka2021buzzy, abstract = {Distributed Database Management Systems (DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance.We propose collecting real-world traces and then applying data generation techniques to synthesize similar realistic traces based on it. Based in this approach, we can obtain workloads for benchmarking, exhibit variability with respect to different aspects of interest while still being similar to the original traces. Varying generation parameters, we are able to support benchmarking what-if scenarios with hypothetical workloads and introduced anomalies.}, address = {New York, NY, USA}, author = {Domaschka, Jörg and Eismann, Simon and Leznik, Mark and Grohmann, Johannes and Kounev, Samuel and Seybold, Daniel}, booktitle = {Companion of the ACM/SPEC International Conference on Performance Engineering}, keywords = {t_visionposition descartes}, pages = {175–178}, publisher = {Association for Computing Machinery}, title = {Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads}, volume = {ICPE '21}, year = 2021 }
%0 Conference Paper %1 domaschka2021buzzy %A Domaschka, Jörg %A Eismann, Simon %A Leznik, Mark %A Grohmann, Johannes %A Kounev, Samuel %A Seybold, Daniel %B Companion of the ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2021 %I Association for Computing Machinery %P 175–178 %R 10.1145/3447545.3451175 %T Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads %U https://doi.org/10.1145/3447545.3451175 %V ICPE '21 %X Distributed Database Management Systems (DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance.We propose collecting real-world traces and then applying data generation techniques to synthesize similar realistic traces based on it. Based in this approach, we can obtain workloads for benchmarking, exhibit variability with respect to different aspects of interest while still being similar to the original traces. Varying generation parameters, we are able to support benchmarking what-if scenarios with hypothetical workloads and introduced anomalies. %@ 9781450383318

SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications. J. Grohmann; M. Straesser; A. Chalbani; S. Eismann; Y. Arian; N. Herbst; N. Peretz; S. Kounev; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
Acceptance Rate: 29%
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Application performance management (APM) tools are useful to observe the performance properties of an application during production. However, APM is normally purely reactive, that is, it can only report about current or past performance degradation. Although some approaches capable of predictive application monitoring have been proposed, they can only report a predicted degradation but cannot explain its root-cause, making it hard to prevent the expected degradation. In this paper, we present SuanMing---a framework for predicting performance degradation of microservice applications running in cloud environments. SuanMing is able to predict future root causes for anticipated performance degradations and therefore aims at preventing performance degradations before they actually occur. We evaluate SuanMing on two realistic microservice applications, TeaStore and TrainTicket, and we show that our approach is able to predict and pinpoint performance degradations with an accuracy of over 90%.
@inproceedings{GrStChEiArHePeKo2021-ICPE, abstract = {Application performance management (APM) tools are useful to observe the performance properties of an application during production. However, APM is normally purely reactive, that is, it can only report about current or past performance degradation. Although some approaches capable of predictive application monitoring have been proposed, they can only report a predicted degradation but cannot explain its root-cause, making it hard to prevent the expected degradation. In this paper, we present SuanMing—a framework for predicting performance degradation of microservice applications running in cloud environments. SuanMing is able to predict future root causes for anticipated performance degradations and therefore aims at preventing performance degradations before they actually occur. We evaluate SuanMing on two realistic microservice applications, TeaStore and TrainTicket, and we show that our approach is able to predict and pinpoint performance degradations with an accuracy of over 90\%.}, address = {New York, NY, USA}, author = {Grohmann, Johannes and Straesser, Martin and Chalbani, Avi and Eismann, Simon and Arian, Yair and Herbst, Nikolas and Peretz, Noam and Kounev, Samuel}, booktitle = {Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {t_full descartes statistical_estimation_and_machine_learning prediction performance}, month = {April}, note = {Acceptance Rate: 29%}, organization = {ACM}, title = {SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications}, year = 2021 }
%0 Conference Paper %1 GrStChEiArHePeKo2021-ICPE %A Grohmann, Johannes %A Straesser, Martin %A Chalbani, Avi %A Eismann, Simon %A Arian, Yair %A Herbst, Nikolas %A Peretz, Noam %A Kounev, Samuel %B Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) %C New York, NY, USA %D 2021 %T SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications %X Application performance management (APM) tools are useful to observe the performance properties of an application during production. However, APM is normally purely reactive, that is, it can only report about current or past performance degradation. Although some approaches capable of predictive application monitoring have been proposed, they can only report a predicted degradation but cannot explain its root-cause, making it hard to prevent the expected degradation. In this paper, we present SuanMing---a framework for predicting performance degradation of microservice applications running in cloud environments. SuanMing is able to predict future root causes for anticipated performance degradations and therefore aims at preventing performance degradations before they actually occur. We evaluate SuanMing on two realistic microservice applications, TeaStore and TrainTicket, and we show that our approach is able to predict and pinpoint performance degradations with an accuracy of over 90%.

A Simulation-based Optimization Framework for Online Adaptation of Networks. S. Herrnleben; J. Grohmann; P. Rygielski; V. Lesch; C. Krupitzer; S. Kounev; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools), H. Song, D. Jiang (Hrsg.) (2021). 513–532.
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Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.
@inproceedings{HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation, abstract = {Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.}, address = {Cham}, author = {Herrnleben, Stefan and Grohmann, Johannes and Rygielski, Pitor and Lesch, Veronika and Krupitzer, Christian and Kounev, Samuel}, booktitle = {Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools)}, editor = {Song, Houbing and Jiang, Dingde}, keywords = {myown t_full descartes networking}, month = {August}, pages = {513–532}, publisher = {Springer International Publishing}, series = {SIMUtools 2020}, title = {A Simulation-based Optimization Framework for Online Adaptation of Networks}, year = 2021 }
%0 Conference Paper %1 HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation %A Herrnleben, Stefan %A Grohmann, Johannes %A Rygielski, Pitor %A Lesch, Veronika %A Krupitzer, Christian %A Kounev, Samuel %B Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) %C Cham %D 2021 %E Song, Houbing %E Jiang, Dingde %I Springer International Publishing %P 513--532 %R 10.1007/978-3-030-72792-5_41 %T A Simulation-based Optimization Framework for Online Adaptation of Networks %X Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations. %@ 978-3-030-72792-5

Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT. T. Prantl; P. Ten; L. Iffländer; S. Herrnleben; A. Dmitrenko; S. Kounev; C. Krupitzer; in 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{PrTeIfHeDmKo2021-ICPE-GroupEncryptionCentralizedBenchmark, author = {Prantl, Thomas and Ten, Peter and Iffländer, Lukas and Herrnleben, Stefan and Dmitrenko, Alexandra and Kounev, Samuel and Krupitzer, Christian}, booktitle = {2021 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {Security t_short descartes sss-group Benchmark Energy\_Efficiency Group\_Encyrption\_Scheme}, month = {April}, series = {ICPE'21}, title = {Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT}, year = 2021 }
%0 Conference Paper %1 PrTeIfHeDmKo2021-ICPE-GroupEncryptionCentralizedBenchmark %A Prantl, Thomas %A Ten, Peter %A Iffländer, Lukas %A Herrnleben, Stefan %A Dmitrenko, Alexandra %A Kounev, Samuel %A Krupitzer, Christian %B 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) %D 2021 %T Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT

Performance Impact Analysis of Securing MQTT Using TLS. T. Prantl; L. Iffländer; S. Herrnleben; S. Engel; S. Kounev; C. Krupitzer; in 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{PrTeIfHeDmKo2021-ICPE-MQTT_TLS, author = {Prantl, Thomas and Iffländer, Lukas and Herrnleben, Stefan and Engel, Simon and Kounev, Samuel and Krupitzer, Christian}, booktitle = {2021 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {Performance Security t_short descartes MQTT sss-group Pub/Sub TLS IoT}, month = {April}, series = {ICPE'21}, title = {Performance Impact Analysis of Securing MQTT Using TLS}, year = 2021 }
%0 Conference Paper %1 PrTeIfHeDmKo2021-ICPE-MQTT_TLS %A Prantl, Thomas %A Iffländer, Lukas %A Herrnleben, Stefan %A Engel, Simon %A Kounev, Samuel %A Krupitzer, Christian %B 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) %D 2021 %T Performance Impact Analysis of Securing MQTT Using TLS

Libra: A Benchmark for Time Series Forecasting Methods. A. Bauer; M. Züfle; S. Eismann; J. Grohmann; N. Herbst; S. Kounev; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
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@inproceedings{bauer2021benchmark, address = {New York, NY, USA}, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Eismann, Simon and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {timeseries t_full descartes metrics_and_benchmarking_methodologies forecasting benchmark}, month = {April}, organization = {ACM}, title = {Libra: A Benchmark for Time Series Forecasting Methods}, year = 2021 }
%0 Conference Paper %1 bauer2021benchmark %A Bauer, André %A Züfle, Marwin %A Eismann, Simon %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) %C New York, NY, USA %D 2021 %R 10.1145/3427921.3450241 %T Libra: A Benchmark for Time Series Forecasting Methods

A Comparison of Mechanisms for Compensating Negative Impacts of System Integration. V. Lesch; C. Krupitzer; K. Stubenrauch; N. Keil; C. Becker; S. Kounev; M. Segata; in Future Generation Computer Systems (2021). 116 117–131.
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@article{lesch2020comparison, author = {Lesch, Veronika and Krupitzer, Christian and Stubenrauch, Kevin and Keil, Nico and Becker, Christian and Kounev, Samuel and Segata, Michele}, journal = {Future Generation Computer Systems}, keywords = {descartes intelligent_transportation_systems cyber-physical_systems internet_of_things compensation system_integration t_journalmagazine platooning}, month = {March}, pages = {117-131}, title = {A Comparison of Mechanisms for Compensating Negative Impacts of System Integration}, volume = 116, year = 2021 }
%0 Journal Article %1 lesch2020comparison %A Lesch, Veronika %A Krupitzer, Christian %A Stubenrauch, Kevin %A Keil, Nico %A Becker, Christian %A Kounev, Samuel %A Segata, Michele %D 2021 %J Future Generation Computer Systems %P 117-131 %R https://doi.org/10.1016/j.future.2020.10.029 %T A Comparison of Mechanisms for Compensating Negative Impacts of System Integration %V 116

Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction. M. Züfle; F. Erhard; S. Kounev; in 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2021).
(to appear)
[ BibTeX ] [ EndNote ]
 
@inproceedings{zufle2021machine, author = {Z{ü}fle, Marwin and Erhard, Florian and Kounev, Samuel}, booktitle = {20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, keywords = {t_full descartes statistical_estimation_and_machine_learning prediction online_monitoring_and_forecasting}, note = {(to appear)}, title = {Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction}, year = 2021 }
%0 Conference Paper %1 zufle2021machine %A Züfle, Marwin %A Erhard, Florian %A Kounev, Samuel %B 20th IEEE International Conference on Machine Learning and Applications (ICMLA) %D 2021 %T Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction

Serverless Applications:Why, When, and How?. S. Eismann; S. Joel; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; in IEEE Software (2021). 38(1) 32–39.
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Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.
@article{eismann2020serverless, abstract = {Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.}, author = {Eismann, Simon and Joel, Scheuner and van Eyk, Erwin and Schwinger, Maximilian and Grohmann, Johannes and Herbst, Nikolas and Abad, Cristina and Iosup, Alexandru}, journal = {IEEE Software}, keywords = {myown descartes t_journalmagazine}, number = 1, pages = {32–39}, title = {Serverless Applications:Why, When, and How?}, volume = 38, year = 2021 }
%0 Journal Article %1 eismann2020serverless %A Eismann, Simon %A Joel, Scheuner %A van Eyk, Erwin %A Schwinger, Maximilian %A Grohmann, Johannes %A Herbst, Nikolas %A Abad, Cristina %A Iosup, Alexandru %D 2021 %J IEEE Software %N 1 %P 32--39 %R 10.1109/MS.2020.3023302 %T Serverless Applications:Why, When, and How? %U https://doi.org/10.1109/MS.2020.3023302 %V 38 %X Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.

An Overview on Approaches for Coordination of Platoons. V. Lesch; M. Breitbach; M. Segata; C. Becker; S. Kounev; C. Krupitzer; in IEEE Transactions on Intelligent Transportation Systems (2021).
Early Access on IEEE Xplore
[ BibTeX ] [ EndNote ] [ Download ]
 
@article{lesch2021overview, author = {Lesch, Veronika and Breitbach, Martin and Segata, Michele and Becker, Christian and Kounev, Samuel and Krupitzer, Christian}, journal = {IEEE Transactions on Intelligent Transportation Systems}, keywords = {descartes intelligent_transportation_systems cyber-physical_systems internet_of_things t_journalmagazine platooning}, note = {Early Access on IEEE Xplore}, title = {An Overview on Approaches for Coordination of Platoons}, year = 2021 }
%0 Journal Article %1 lesch2021overview %A Lesch, Veronika %A Breitbach, Martin %A Segata, Michele %A Becker, Christian %A Kounev, Samuel %A Krupitzer, Christian %D 2021 %J IEEE Transactions on Intelligent Transportation Systems %T An Overview on Approaches for Coordination of Platoons

2020 [ nach oben ]

A Review of Serverless Use Cases and their Characteristics S. Eismann; J. Scheuner; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The serverless computing paradigm promises many desirable properties for cloud applications - low-cost, fine-grained deployment, and management-free operation. Consequently, the paradigm has underwent rapid growth: there currently exist tens of serverless platforms and all global cloud providers host serverless operations. To help tune existing platforms, guide the design of new serverless approaches, and overall contribute to understanding this paradigm, in this work we present a long-term, comprehensive effort to identify, collect, and characterize serverless use cases. We survey 89 use cases, sourced from white and grey literature, and from consultations with experts in areas such as scientific computing. We study each use case using 24 characteristics, including general aspects, but also workload, application, and requirements. When the use cases employ workflows, we further analyze their characteristics. Overall, we hope our study will be useful for both academia and industry, and encourage the community to further share and communicate their use cases.
@techreport{eismann2020review, abstract = {The serverless computing paradigm promises many desirable properties for cloud applications - low-cost, fine-grained deployment, and management-free operation. Consequently, the paradigm has underwent rapid growth: there currently exist tens of serverless platforms and all global cloud providers host serverless operations. To help tune existing platforms, guide the design of new serverless approaches, and overall contribute to understanding this paradigm, in this work we present a long-term, comprehensive effort to identify, collect, and characterize serverless use cases. We survey 89 use cases, sourced from white and grey literature, and from consultations with experts in areas such as scientific computing. We study each use case using 24 characteristics, including general aspects, but also workload, application, and requirements. When the use cases employ workflows, we further analyze their characteristics. Overall, we hope our study will be useful for both academia and industry, and encourage the community to further share and communicate their use cases.}, author = {Eismann, Simon and Scheuner, Joel and van Eyk, Erwin and Schwinger, Maximilian and Grohmann, Johannes and Herbst, Nikolas and Abad, Cristina and Iosup, Alexandru}, keywords = {myown descartes t_techreport}, month = {June}, publisher = {SPEC RG}, title = {A Review of Serverless Use Cases and their Characteristics}, year = 2020 }
%0 Report %1 eismann2020review %A Eismann, Simon %A Scheuner, Joel %A van Eyk, Erwin %A Schwinger, Maximilian %A Grohmann, Johannes %A Herbst, Nikolas %A Abad, Cristina %A Iosup, Alexandru %D 2020 %I SPEC RG %T A Review of Serverless Use Cases and their Characteristics %X The serverless computing paradigm promises many desirable properties for cloud applications - low-cost, fine-grained deployment, and management-free operation. Consequently, the paradigm has underwent rapid growth: there currently exist tens of serverless platforms and all global cloud providers host serverless operations. To help tune existing platforms, guide the design of new serverless approaches, and overall contribute to understanding this paradigm, in this work we present a long-term, comprehensive effort to identify, collect, and characterize serverless use cases. We survey 89 use cases, sourced from white and grey literature, and from consultations with experts in areas such as scientific computing. We study each use case using 24 characteristics, including general aspects, but also workload, application, and requirements. When the use cases employ workflows, we further analyze their characteristics. Overall, we hope our study will be useful for both academia and industry, and encourage the community to further share and communicate their use cases.

Predicting Performance Degradations of Black-box Microservice Applications M. Straesser; (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@presentation{straesser2020talk, author = {Straesser, Martin}, keywords = {t_talk descartes microservice performance_prediction sys:relevantfor:se-group}, title = {Predicting Performance Degradations of Black-box Microservice Applications}, year = 2020 }
%0 Generic %1 straesser2020talk %A Straesser, Martin %D 2020 %T Predicting Performance Degradations of Black-box Microservice Applications %U https://www.performance-symposium.org/2020/

Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning. M. D’Angelo; S. Ghahremani; S. Gerasimou; J. Grohmann; I. Nunes; S. Tomforde; E. Pournaras; in 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (2020). 121–126.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.
@conference{DaGhGeGrNuToPo2020-eCas-LearningInCAS, abstract = {Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.}, author = {D'Angelo, Mirko and Ghahremani, Sona and Gerasimou, Simos and Grohmann, Johannes and Nunes, Ingrid and Tomforde, Sven and Pournaras, Evangelos}, booktitle = {2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {Statistical_estimation_and_machine_learning myown descartes t_workshop}, pages = {121–126}, publisher = {IEEE}, title = {Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning}, year = 2020 }
%0 Generic %1 DaGhGeGrNuToPo2020-eCas-LearningInCAS %A D'Angelo, Mirko %A Ghahremani, Sona %A Gerasimou, Simos %A Grohmann, Johannes %A Nunes, Ingrid %A Tomforde, Sven %A Pournaras, Evangelos %B 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2020 %I IEEE %P 121--126 %R 10.1109/ACSOS-C51401.2020.00042 %T Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning %U https://doi.org/10.1109/ACSOS-C51401.2020.00042 %X Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.

Optimizing Parametric Dependencies for Incremental Performance Model Extraction. S. Voneva; M. Mazkatli; J. Grohmann; A. Koziolek; in Companion of the 14th European Conference Software Architecture (ECSA 2020), H. Muccini, P. Avgeriou, B. Buhnova, J. Cámara, M. Caporuscio, M. Franzago, A. Koziolek, P. Scandurra, C. Trubiani, D. Weyns, U. Zdun (Hrsg.) (2020). (Bd. 1269) 228–240.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Model-based performance prediction in agile software development promises to evaluate design alternatives and to reduce the cost of performance tests. To minimize the differences between a real software and its performance model, parametric dependencies are introduced. They express how the performance model parameters (such as loop iteration count, branch transition probabilities, resource demands, and external service call arguments) depend on impacting factors like the input data. The approaches that perform model-based performance prediction in agile software development have two major shortcomings: they are either costly because they do not update the performance models automatically after each commit, or do not consider more complex parametric dependencies than linear. This work extends an approach for continuous integration of performance model during agile development. Our extension aims to optimize the learning of parametric dependencies with a genetic programming algorithm to be able to detect non-linear dependencies. The case study results show that using genetic programming enables detecting more complex dependencies and improves the accuracy of the updated performance model.
@inproceedings{VoMaGrKo-ECSA-Dependencies, abstract = {Model-based performance prediction in agile software development promises to evaluate design alternatives and to reduce the cost of performance tests. To minimize the differences between a real software and its performance model, parametric dependencies are introduced. They express how the performance model parameters (such as loop iteration count, branch transition probabilities, resource demands, and external service call arguments) depend on impacting factors like the input data. The approaches that perform model-based performance prediction in agile software development have two major shortcomings: they are either costly because they do not update the performance models automatically after each commit, or do not consider more complex parametric dependencies than linear. This work extends an approach for continuous integration of performance model during agile development. Our extension aims to optimize the learning of parametric dependencies with a genetic programming algorithm to be able to detect non-linear dependencies. The case study results show that using genetic programming enables detecting more complex dependencies and improves the accuracy of the updated performance model.}, address = {Cham}, author = {Voneva, Sonya and Mazkatli, Manar and Grohmann, Johannes and Koziolek, Anne}, booktitle = {Companion of the 14th European Conference Software Architecture ({ECSA} 2020)}, editor = {Muccini, Henry and Avgeriou, Paris and Buhnova, Barbora and C{{á}}mara, Javier and Caporuscio, Mauro and Franzago, Mirco and Koziolek, Anne and Scandurra, Patrizia and Trubiani, Catia and Weyns, Danny and Zdun, Uwe}, keywords = {myown descartes statistical_estimation_and_machine_learning parametric_dependencies prediction performance t_workshop}, month = {September}, pages = {228–240}, publisher = {Springer}, series = {Communications in Computer and Information Science}, title = {Optimizing Parametric Dependencies for Incremental Performance Model Extraction}, volume = 1269, year = 2020 }
%0 Conference Paper %1 VoMaGrKo-ECSA-Dependencies %A Voneva, Sonya %A Mazkatli, Manar %A Grohmann, Johannes %A Koziolek, Anne %B Companion of the 14th European Conference Software Architecture (ECSA 2020) %C Cham %D 2020 %E Muccini, Henry %E Avgeriou, Paris %E Buhnova, Barbora %E Cámara, Javier %E Caporuscio, Mauro %E Franzago, Mirco %E Koziolek, Anne %E Scandurra, Patrizia %E Trubiani, Catia %E Weyns, Danny %E Zdun, Uwe %I Springer %P 228--240 %R 10.1007/978-3-030-59155-7_17 %T Optimizing Parametric Dependencies for Incremental Performance Model Extraction %U https://doi.org/10.1007/978-3-030-59155-7_17 %V 1269 %X Model-based performance prediction in agile software development promises to evaluate design alternatives and to reduce the cost of performance tests. To minimize the differences between a real software and its performance model, parametric dependencies are introduced. They express how the performance model parameters (such as loop iteration count, branch transition probabilities, resource demands, and external service call arguments) depend on impacting factors like the input data. The approaches that perform model-based performance prediction in agile software development have two major shortcomings: they are either costly because they do not update the performance models automatically after each commit, or do not consider more complex parametric dependencies than linear. This work extends an approach for continuous integration of performance model during agile development. Our extension aims to optimize the learning of parametric dependencies with a genetic programming algorithm to be able to detect non-linear dependencies. The case study results show that using genetic programming enables detecting more complex dependencies and improves the accuracy of the updated performance model.

Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario. T. Prantl; P. Ten; L. Iffländer; A. Dmitrenko; S. Kounev; C. Krupitzer; in 2020 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020).
Acceptance Rate: 27%
[ Abstract ] [ BibTeX ] [ EndNote ]
 
New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things — cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms — to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, manyto-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-tomany communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.
@inproceedings{PrTeIfDmKo2020-MASCOTS-GroupEncryption, abstract = {New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things — cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms — to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, manyto-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-tomany communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.}, author = {Prantl, Thomas and Ten, Peter and Iffländer, Lukas and Dmitrenko, Alexandra and Kounev, Samuel and Krupitzer, Christian}, booktitle = {2020 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {Internet_of_Things myown Security t_full descartes Group_Encryption sss-group Performance_Evaluation}, month = {November}, note = {Acceptance Rate: 27\%}, series = {MASCOTS '20}, title = {Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario}, year = 2020 }
%0 Conference Paper %1 PrTeIfDmKo2020-MASCOTS-GroupEncryption %A Prantl, Thomas %A Ten, Peter %A Iffländer, Lukas %A Dmitrenko, Alexandra %A Kounev, Samuel %A Krupitzer, Christian %B 2020 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) %D 2020 %T Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario %X New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things — cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms — to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, manyto-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-tomany communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.

An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things. C. Krupitzer; T. Temizer; T. Prantl; C. Raibulet; in IEEE Access (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.
@article{ieeeaccess2020:sasdesignpatterns, abstract = {The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.}, author = {Krupitzer, Christian and Temizer, Timur and Prantl, Thomas and Raibulet, Claudia}, journal = {IEEE Access}, keywords = {descartes cyber-physical_systems internet_of_things t_journalmagazine sys:relevantfor:se-group self-adaptive-systems}, title = {An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things}, year = 2020 }
%0 Journal Article %1 ieeeaccess2020:sasdesignpatterns %A Krupitzer, Christian %A Temizer, Timur %A Prantl, Thomas %A Raibulet, Claudia %D 2020 %J IEEE Access %T An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things %X The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.

REACT: A Model-Based Runtime Environment for Adapting Communication Systems. M. Pfannemüller; M. Breitbach; C. Krupitzer; M. Weckesser; C. Becker; B. Schmerl; A. Schürr; in Proceedings of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020) (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Trends such as the Internet of Things or edge computing lead to a growing number of networked devices. Hence, it is becoming increasingly important to manage communication systems at runtime. Adding self-adaptive capabilities is one approach to reduce administrative effort and cope with changing execution contexts. Existing frameworks for building self-adaptive software can help to reduce development effort in general. Yet, they are neither tailored towards the use in communication systems nor easily usable without profound knowledge in self-adaptive systems development. In this paper, we propose REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. It addresses the heterogeneity and distribution aspects of networks and reduces development effort. REACT empowers developers of communication systems to add adaptive behavior without having experience in self-adaptive systems development. We show the effectiveness and efficiency of our prototype in an experimental evaluation based on two distinct use cases from the communication systems domain: cloud resource management and software-defined networking. The first use case includes a comparison with Rainbow, which represents a state-of-the-art model-based framework for building self-adaptive systems. The second use case applies REACT in a sophisticated, real-world communication system scenario.
@conference{acsos2020:react, abstract = {Trends such as the Internet of Things or edge computing lead to a growing number of networked devices. Hence, it is becoming increasingly important to manage communication systems at runtime. Adding self-adaptive capabilities is one approach to reduce administrative effort and cope with changing execution contexts. Existing frameworks for building self-adaptive software can help to reduce development effort in general. Yet, they are neither tailored towards the use in communication systems nor easily usable without profound knowledge in self-adaptive systems development. In this paper, we propose REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. It addresses the heterogeneity and distribution aspects of networks and reduces development effort. REACT empowers developers of communication systems to add adaptive behavior without having experience in self-adaptive systems development. We show the effectiveness and efficiency of our prototype in an experimental evaluation based on two distinct use cases from the communication systems domain: cloud resource management and software-defined networking. The first use case includes a comparison with Rainbow, which represents a state-of-the-art model-based framework for building self-adaptive systems. The second use case applies REACT in a sophisticated, real-world communication system scenario.}, author = {Pfannemüller, Martin and Breitbach, Martin and Krupitzer, Christian and Weckesser, Markus and Becker, Christian and Schmerl, Bradley and Schürr, Andy}, booktitle = {Proceedings of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020)}, keywords = {t_full descartes cyber-physical_systems internet_of_things sys:relevantfor:se-group self-adaptive-systems}, title = {REACT: A Model-Based Runtime Environment for Adapting Communication Systems}, year = 2020 }
%0 Generic %1 acsos2020:react %A Pfannemüller, Martin %A Breitbach, Martin %A Krupitzer, Christian %A Weckesser, Markus %A Becker, Christian %A Schmerl, Bradley %A Schürr, Andy %B Proceedings of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020) %D 2020 %T REACT: A Model-Based Runtime Environment for Adapting Communication Systems %X Trends such as the Internet of Things or edge computing lead to a growing number of networked devices. Hence, it is becoming increasingly important to manage communication systems at runtime. Adding self-adaptive capabilities is one approach to reduce administrative effort and cope with changing execution contexts. Existing frameworks for building self-adaptive software can help to reduce development effort in general. Yet, they are neither tailored towards the use in communication systems nor easily usable without profound knowledge in self-adaptive systems development. In this paper, we propose REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. It addresses the heterogeneity and distribution aspects of networks and reduces development effort. REACT empowers developers of communication systems to add adaptive behavior without having experience in self-adaptive systems development. We show the effectiveness and efficiency of our prototype in an experimental evaluation based on two distinct use cases from the communication systems domain: cloud resource management and software-defined networking. The first use case includes a comparison with Rainbow, which represents a state-of-the-art model-based framework for building self-adaptive systems. The second use case applies REACT in a sophisticated, real-world communication system scenario.

A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation. M. Züfle; S. Kounev; in Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications (2020).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{ZuKo-AAIA2020-Framework, author = {Z{ü}fle, Marwin and Kounev, Samuel}, booktitle = {Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications}, keywords = {t_short descartes statistical_estimation_and_machine_learning prediction online_monitoring_and_forecasting}, month = {September}, title = {A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation}, year = 2020 }
%0 Conference Paper %1 ZuKo-AAIA2020-Framework %A Züfle, Marwin %A Kounev, Samuel %B Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications %D 2020 %T A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation

Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data. F. Spiess; J. Friesslich; K. Tobias; S. Kounev; N. Strobel; in International Journal of Mechanical Engineering and Robotics Research (IJMERR), (P. R. (Chunhui) Yang, Hrsg.) (2020). 9(12) 1532–1540.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]
 
This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.
@article{yang2020survey, abstract = {This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.}, author = {Spiess, Florian and Friesslich, Jonas and Tobias, Kaupp and Kounev, Samuel and Strobel, Norbert}, editor = {Yang, Prof Richard (Chunhui)}, journal = {International Journal of Mechanical Engineering and Robotics Research (IJMERR)}, keywords = {comparison descartes robotics survey t_interdisciplinary t_journalmagazine rgb}, month = {dec}, number = 12, pages = {1532-1540}, title = {Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data}, volume = 9, year = 2020 }
%0 Journal Article %1 yang2020survey %A Spiess, Florian %A Friesslich, Jonas %A Tobias, Kaupp %A Kounev, Samuel %A Strobel, Norbert %D 2020 %E Yang, Prof Richard (Chunhui) %J International Journal of Mechanical Engineering and Robotics Research (IJMERR) %N 12 %P 1532-1540 %T Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data %U http://www.ijmerr.com/index.php?m=content&c=index&a=show&catid=185&id=1529 %V 9 %X This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.

An IoT Network Emulator for Analyzing the Influence of Varying Network Quality. S. Herrnleben; R. Ailabouni; J. Grohmann; T. Prantl; C. Krupitzer; S. Kounev; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
IoT devices often communicate over wireless or cellular networks with varying connection quality. These fluctuations are caused, among others, by the free-space path loss (FSPL), buildings, topological obstacles, weather, and mobility of the receiver. Varying signal quality affects bandwidth, transmission delays, packet loss, and jitter. Mobile IoT applications exposed to varying connection characteristics have to handle such variations and take them into account during development and testing. However, tests in real mobile networks are complex and challenging to reproduce. Therefore, network emulators can be used to simulate the behavior of real-world networks by adding artificial disturbance. However, existing network emulators often require a lot of technical knowledge and complex setup. Integrating such emulators into automated software testing pipelines could be a challenging task. In this paper, we propose a framework for emulating IoT networks with varying quality characteristics. An existing base emulator is used and integrated into our framework enabling the user to utilize it without extensive network expertise and configuration effort. The evaluation proves that our framework can simulate a variety of different network quality characteristics as well as emulating real-world network traces.
@inproceedings{HeAiGrPrKrKo2020-SIMUtools-IoT-Network-Emulator, abstract = {IoT devices often communicate over wireless or cellular networks with varying connection quality. These fluctuations are caused, among others, by the free-space path loss (FSPL), buildings, topological obstacles, weather, and mobility of the receiver. Varying signal quality affects bandwidth, transmission delays, packet loss, and jitter. Mobile IoT applications exposed to varying connection characteristics have to handle such variations and take them into account during development and testing. However, tests in real mobile networks are complex and challenging to reproduce. Therefore, network emulators can be used to simulate the behavior of real-world networks by adding artificial disturbance. However, existing network emulators often require a lot of technical knowledge and complex setup. Integrating such emulators into automated software testing pipelines could be a challenging task. In this paper, we propose a framework for emulating IoT networks with varying quality characteristics. An existing base emulator is used and integrated into our framework enabling the user to utilize it without extensive network expertise and configuration effort. The evaluation proves that our framework can simulate a variety of different network quality characteristics as well as emulating real-world network traces.}, author = {Herrnleben, Stefan and Ailabouni, Rudy and Grohmann, Johannes and Prantl, Thomas and Krupitzer, Christian and Kounev, Samuel}, booktitle = {Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools)}, keywords = {myown t_full descartes internet_of_things networking}, month = {August}, series = {SIMUtools 2020}, title = {An IoT Network Emulator for Analyzing the Influence of Varying Network Quality}, year = 2020 }
%0 Conference Paper %1 HeAiGrPrKrKo2020-SIMUtools-IoT-Network-Emulator %A Herrnleben, Stefan %A Ailabouni, Rudy %A Grohmann, Johannes %A Prantl, Thomas %A Krupitzer, Christian %A Kounev, Samuel %B Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) %D 2020 %T An IoT Network Emulator for Analyzing the Influence of Varying Network Quality %X IoT devices often communicate over wireless or cellular networks with varying connection quality. These fluctuations are caused, among others, by the free-space path loss (FSPL), buildings, topological obstacles, weather, and mobility of the receiver. Varying signal quality affects bandwidth, transmission delays, packet loss, and jitter. Mobile IoT applications exposed to varying connection characteristics have to handle such variations and take them into account during development and testing. However, tests in real mobile networks are complex and challenging to reproduce. Therefore, network emulators can be used to simulate the behavior of real-world networks by adding artificial disturbance. However, existing network emulators often require a lot of technical knowledge and complex setup. Integrating such emulators into automated software testing pipelines could be a challenging task. In this paper, we propose a framework for emulating IoT networks with varying quality characteristics. An existing base emulator is used and integrated into our framework enabling the user to utilize it without extensive network expertise and configuration effort. The evaluation proves that our framework can simulate a variety of different network quality characteristics as well as emulating real-world network traces.

Elasticity of Cloud Platforms. N. Herbst; A. Bauer; S. Kounev; in Systems Benchmarking: For Scientists and Engineers, S. Kounev, K.-D. Lange, J. von Kistowski (Hrsg.) (2020). 319–340.
[ BibTeX ] [ EndNote ]
 
@inproceedings{bauer2020bookelasticity, author = {Herbst, Nikolas and Bauer, Andr{é} and Kounev, Samuel}, booktitle = {{Systems Benchmarking: For Scientists and Engineers}}, editor = {Kounev, Samuel and Lange, Klaus-Dieter and von Kistowski, J{ó}akim}, keywords = {cloud descartes metrics_and_benchmarking_methodologies elasticity t_bookchapter}, month = 1, organization = {Springer}, pages = {319-340}, title = {{Elasticity of Cloud Platforms}}, year = 2020 }
%0 Conference Paper %1 bauer2020bookelasticity %A Herbst, Nikolas %A Bauer, André %A Kounev, Samuel %B Systems Benchmarking: For Scientists and Engineers %D 2020 %E Kounev, Samuel %E Lange, Klaus-Dieter %E von Kistowski, Jóakim %P 319-340 %T Elasticity of Cloud Platforms

Incremental Calibration of Architectural Performance Models with Parametric Dependencies. M. Mazkatli; D. Monschein; J. Grohmann; A. Koziolek; in 2020 IEEE International Conference on Software Architecture (ICSA 2020) (2020). 23–34.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.
@inproceedings{MaMoGrKo2020-ICSA, abstract = {Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.}, author = {Mazkatli, Manar and Monschein, David and Grohmann, Johannes and Koziolek, Anne}, booktitle = {2020 IEEE International Conference on Software Architecture (ICSA 2020)}, keywords = {myown modeling t_full descartes statistical_estimation_and_machine_learning Automated_model_learning prediction performance}, month = {March}, pages = {23–34}, publisher = {IEEE}, title = {Incremental Calibration of Architectural Performance Models with Parametric Dependencies}, year = 2020 }
%0 Conference Paper %1 MaMoGrKo2020-ICSA %A Mazkatli, Manar %A Monschein, David %A Grohmann, Johannes %A Koziolek, Anne %B 2020 IEEE International Conference on Software Architecture (ICSA 2020) %D 2020 %I IEEE %P 23--34 %R 10.1109/ICSA47634.2020.00011 %T Incremental Calibration of Architectural Performance Models with Parametric Dependencies %U https://doi.org/10.1109/ICSA47634.2020.00011 %X Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.

Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS. J. Grohmann; D. Seybold; S. Eismann; M. Leznik; S. Kounev; J. Domaschka; in 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020). 1–8.
Acceptance Rate: 27%
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Correctly configuring a distributed database management system (DBMS) deployed in a cloud environment for maximizing performance poses many challenges to operators. Even if the entire configuration spectrum could be measured directly, which is often infeasible due to the multitude of parameters, single measurements are subject to random variations and need to be repeated multiple times. In this work, we propose Baloo, a framework for systematically measuring and modeling different performance-relevant configurations of distributed DBMS in cloud environments. Baloo dynamically estimates the required number of configurations, as well as the number of required measurement repetitions per configuration based on a desired target accuracy. We evaluate Baloo based on a data set consisting of 900 DBMS configuration measurements conducted in our private cloud setup. Our evaluation shows that the highly configurable framework is able to achieve a prediction error of up to 12% while saving 80% of the measurement effort. We also publish all code and the acquired data set to foster future research.
@inproceedings{GrSeEiLeKoDo2020-MASCOTS-DBMSPerformance, abstract = {Correctly configuring a distributed database management system (DBMS) deployed in a cloud environment for maximizing performance poses many challenges to operators. Even if the entire configuration spectrum could be measured directly, which is often infeasible due to the multitude of parameters, single measurements are subject to random variations and need to be repeated multiple times. In this work, we propose Baloo, a framework for systematically measuring and modeling different performance-relevant configurations of distributed DBMS in cloud environments. Baloo dynamically estimates the required number of configurations, as well as the number of required measurement repetitions per configuration based on a desired target accuracy. We evaluate Baloo based on a data set consisting of 900 DBMS configuration measurements conducted in our private cloud setup. Our evaluation shows that the highly configurable framework is able to achieve a prediction error of up to 12% while saving 80% of the measurement effort. We also publish all code and the acquired data set to foster future research.}, author = {Grohmann, Johannes and Seybold, Daniel and Eismann, Simon and Leznik, Mark and Kounev, Samuel and Domaschka, Jörg}, booktitle = {2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {myown t_full performance_modeling descartes statistical_estimation_and_machine_learning prediction performance automated_model_learning}, month = {November}, note = {Acceptance Rate: 27%}, pages = {1–8}, publisher = {IEEE}, series = {MASCOTS '20}, title = {Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS}, year = 2020 }
%0 Conference Paper %1 GrSeEiLeKoDo2020-MASCOTS-DBMSPerformance %A Grohmann, Johannes %A Seybold, Daniel %A Eismann, Simon %A Leznik, Mark %A Kounev, Samuel %A Domaschka, Jörg %B 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) %D 2020 %I IEEE %P 1--8 %R 10.1109/MASCOTS50786.2020.9285960 %T Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS %U https://doi.org/10.1109/MASCOTS50786.2020.9285960 %X Correctly configuring a distributed database management system (DBMS) deployed in a cloud environment for maximizing performance poses many challenges to operators. Even if the entire configuration spectrum could be measured directly, which is often infeasible due to the multitude of parameters, single measurements are subject to random variations and need to be repeated multiple times. In this work, we propose Baloo, a framework for systematically measuring and modeling different performance-relevant configurations of distributed DBMS in cloud environments. Baloo dynamically estimates the required number of configurations, as well as the number of required measurement repetitions per configuration based on a desired target accuracy. We evaluate Baloo based on a data set consisting of 900 DBMS configuration measurements conducted in our private cloud setup. Our evaluation shows that the highly configurable framework is able to achieve a prediction error of up to 12% while saving 80% of the measurement effort. We also publish all code and the acquired data set to foster future research.

Systems Benchmarking S. Kounev; K.-D. Lange; J. von Kistowski; (2020). (1. Aufl.) Springer International Publishing.
[ BibTeX ] [ EndNote ]
 
@book{KoLaKi-2020-SystemsBenchmarking, author = {Kounev, Samuel and Lange, Klaus-Dieter and von Kistowski, Jóakim}, edition = 1, keywords = {descartes t_book}, publisher = {Springer International Publishing}, title = {{Systems Benchmarking}}, year = 2020 }
%0 Book %1 KoLaKi-2020-SystemsBenchmarking %A Kounev, Samuel %A Lange, Klaus-Dieter %A von Kistowski, Jóakim %D 2020 %I Springer International Publishing %T Systems Benchmarking %7 1

Quantifying measurement quality and load distribution in Tor. A. Greubel; S. Pohl; S. Kounev; in Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC 2020) (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Tor is a widely used anonymization network. Traffic is routed over different relay nodes to conceal the communication partners. However, if a single relay handles too much traffic, de-anonymization attacks are possible. The Tor Load Balancing Mechanism (TLBM) is responsible for balanced and secure load distribution. It must verify that relays cannot attract more traffic than they should by lying about their self-reported bandwidth. This work shows that the current bandwidth measurement method used for bandwidth verification is not suitable to verify the bandwidth of many relays. Most importantly, multiple measurements of high-bandwidth relays are uncorrelated to each other. Furthermore, we analyze the current load distribution in Tor. We show that the current load distribution reduces the resources necessary for several large-scale de-anonymization attacks by more than 80%. Additionally, as Tor favors fast relays during path selection, verifiable relays only handle a small fraction of Tor’s traffic. More precisely, we show that only 7.21% of all paths consist of entry and exit relays verifiable by measurements. We discuss these results’ security implications and argue that future TLBM research should focus at least as much on secure load distribution as on high traffic performance.
@article{greubel2020quantifying, abstract = {Tor is a widely used anonymization network. Traffic is routed over different relay nodes to conceal the communication partners. However, if a single relay handles too much traffic, de-anonymization attacks are possible. The Tor Load Balancing Mechanism (TLBM) is responsible for balanced and secure load distribution. It must verify that relays cannot attract more traffic than they should by lying about their self-reported bandwidth. This work shows that the current bandwidth measurement method used for bandwidth verification is not suitable to verify the bandwidth of many relays. Most importantly, multiple measurements of high-bandwidth relays are uncorrelated to each other. Furthermore, we analyze the current load distribution in Tor. We show that the current load distribution reduces the resources necessary for several large-scale de-anonymization attacks by more than 80\%. Additionally, as Tor favors fast relays during path selection, verifiable relays only handle a small fraction of Tor’s traffic. More precisely, we show that only 7.21\% of all paths consist of entry and exit relays verifiable by measurements. We discuss these results’ security implications and argue that future TLBM research should focus at least as much on secure load distribution as on high traffic performance.}, author = {Greubel, Andre and Pohl, Steffen and Kounev, Samuel}, journal = {Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC 2020)}, keywords = {balancing t_full TLBM descartes tor}, title = {Quantifying measurement quality and load distribution in Tor}, year = 2020 }
%0 Journal Article %1 greubel2020quantifying %A Greubel, Andre %A Pohl, Steffen %A Kounev, Samuel %D 2020 %J Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC 2020) %T Quantifying measurement quality and load distribution in Tor %X Tor is a widely used anonymization network. Traffic is routed over different relay nodes to conceal the communication partners. However, if a single relay handles too much traffic, de-anonymization attacks are possible. The Tor Load Balancing Mechanism (TLBM) is responsible for balanced and secure load distribution. It must verify that relays cannot attract more traffic than they should by lying about their self-reported bandwidth. This work shows that the current bandwidth measurement method used for bandwidth verification is not suitable to verify the bandwidth of many relays. Most importantly, multiple measurements of high-bandwidth relays are uncorrelated to each other. Furthermore, we analyze the current load distribution in Tor. We show that the current load distribution reduces the resources necessary for several large-scale de-anonymization attacks by more than 80%. Additionally, as Tor favors fast relays during path selection, verifiable relays only handle a small fraction of Tor’s traffic. More precisely, we show that only 7.21% of all paths consist of entry and exit relays verifiable by measurements. We discuss these results’ security implications and argue that future TLBM research should focus at least as much on secure load distribution as on high traffic performance.

SIMPL: Secure IoT Management Platform. T. Prantl; A. E. Ben Yahya; A. Dmitrienko; S. Kounev; F. Lipp; D. Hock; C. Rathfelder; M. Hofherr; in ITG Workshop on IT Security (ITSec) (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However,despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL
@inproceedings{PrYaDmKoLiHoRaHo2020-ITSec-SIMPL, abstract = {The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However,despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL}, author = {Prantl, Thomas and Ben Yahya, Ala Eddine and Dmitrienko, Alexandra and Kounev, Samuel and Lipp, Fabian and Hock, David and Rathfelder, Christoph and Hofherr, Martin}, booktitle = {ITG Workshop on IT Security (ITSec)}, keywords = {SIMPL myown descartes internet_of_things t_workshop}, title = {SIMPL: Secure IoT Management Platform}, year = 2020 }
%0 Conference Paper %1 PrYaDmKoLiHoRaHo2020-ITSec-SIMPL %A Prantl, Thomas %A Ben Yahya, Ala Eddine %A Dmitrienko, Alexandra %A Kounev, Samuel %A Lipp, Fabian %A Hock, David %A Rathfelder, Christoph %A Hofherr, Martin %B ITG Workshop on IT Security (ITSec) %D 2020 %T SIMPL: Secure IoT Management Platform %X The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However,despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL

The impact of the German strategy for containment of Coronavirus SARS-CoV-2 on the training characteristics, physical activity, sleep of highly trained kayakers and canoeists: A retrospective observational study. C. Zinner; M. Matzka; R. Leppich; S. Kounev; H. Holmberg; B. Sperlich; in Frontiers in Sports and Active Living (2020). 2 127.
[ BibTeX ] [ EndNote ]
 
@article{zinner2020impact, author = {Zinner, Christoph and Matzka, Manuel and Leppich, Robert and Kounev, Samuel and Holmberg, HC and Sperlich, Billy}, journal = {Frontiers in Sports and Active Living}, keywords = {myown descartes t_interdisciplinary t_journalmagazine health covid19}, pages = 127, publisher = {Frontiers}, title = {The impact of the German strategy for containment of Coronavirus SARS-CoV-2 on the training characteristics, physical activity, sleep of highly trained kayakers and canoeists: A retrospective observational study}, volume = 2, year = 2020 }
%0 Journal Article %1 zinner2020impact %A Zinner, Christoph %A Matzka, Manuel %A Leppich, Robert %A Kounev, Samuel %A Holmberg, HC %A Sperlich, Billy %D 2020 %I Frontiers %J Frontiers in Sports and Active Living %P 127 %T The impact of the German strategy for containment of Coronavirus SARS-CoV-2 on the training characteristics, physical activity, sleep of highly trained kayakers and canoeists: A retrospective observational study %V 2

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. J. Grohmann; N. Herbst; A. Chalbani; Y. Arian; N. Peretz; S. Kounev; in Computers (2020). 9(1) 10.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ]
 
Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.
@article{grohmann2020-MDPI-taxonomy, abstract = {Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.}, author = {Grohmann, Johannes and Herbst, Nikolas and Chalbani, Avi and Arian, Yair and Peretz, Noam and Kounev, Samuel}, journal = {Computers}, keywords = {descartes performance t_journalmagazine se2 automated_model_learning}, number = 1, pages = 10, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, title = {A Taxonomy of Techniques for SLO Failure Prediction in Software Systems}, volume = 9, year = 2020 }
%0 Journal Article %1 grohmann2020-MDPI-taxonomy %A Grohmann, Johannes %A Herbst, Nikolas %A Chalbani, Avi %A Arian, Yair %A Peretz, Noam %A Kounev, Samuel %D 2020 %I Multidisciplinary Digital Publishing Institute (MDPI) %J Computers %N 1 %P 10 %R 10.3390/computers9010010 %T A Taxonomy of Techniques for SLO Failure Prediction in Software Systems %U https://doi.org/10.3390/computers9010010 %V 9 %X Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.

Beyond Microbenchmarks: The SPEC-RG Vision for A Comprehensive Serverless Benchmark. E. van Eyk; J. Scheuner; S. Eismann; C. Abad; A. Iosup; in Companion of the 2020 ACM/SPEC International Conference on Performance Engineering (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.
@inproceedings{10.1145/3185768.3186293, abstract = {Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.}, author = {van Eyk, Erwin and Scheuner, Joel and Eismann, Simon and Abad, Cristina and Iosup, Alexandru}, booktitle = {Companion of the 2020 ACM/SPEC International Conference on Performance Engineering}, keywords = {t_visionposition Metrics_and_benchmarking_methodologies Performance descartes SPEC}, title = {{Beyond Microbenchmarks: The SPEC-RG Vision for A Comprehensive Serverless Benchmark}}, year = 2020 }
%0 Conference Paper %1 10.1145/3185768.3186293 %A van Eyk, Erwin %A Scheuner, Joel %A Eismann, Simon %A Abad, Cristina %A Iosup, Alexandru %B Companion of the 2020 ACM/SPEC International Conference on Performance Engineering %D 2020 %T Beyond Microbenchmarks: The SPEC-RG Vision for A Comprehensive Serverless Benchmark %X Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.

Predicting the Costs of Serverless Workflows. S. Eismann; J. Grohmann; E. van Eyk; N. Herbst; S. Kounev; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020). 265–276.
Acceptance Rate: 23.4% (15/64)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.
@inproceedings{EiGrEyHeKo2020-ICPE-ServerlessWorkflows, abstract = {Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.}, address = {New York, NY, USA}, author = {Eismann, Simon and Grohmann, Johannes and van Eyk, Erwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {t_full descartes se2}, month = {April}, note = {{Acceptance Rate: 23.4% (15/64)}}, pages = {265–276}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '20}, title = {Predicting the Costs of Serverless Workflows}, year = 2020 }
%0 Conference Paper %1 EiGrEyHeKo2020-ICPE-ServerlessWorkflows %A Eismann, Simon %A Grohmann, Johannes %A van Eyk, Erwin %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) %C New York, NY, USA %D 2020 %I Association for Computing Machinery (ACM) %P 265--276 %R 10.1145/3358960.3379133 %T Predicting the Costs of Serverless Workflows %U https://doi.org/10.1145/3358960.3379133 %X Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%. %@ 9781450369916

Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. A. Bauer; M. Züfle; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{BaZuHeKoCu-ICDE-Telescope, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 36th International Conference on Data Engineering (ICDE)}, keywords = {Tool Telescope Online_monitoring_and_forecasting t_short descartes Prediction}, month = {April}, title = {Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field}, year = 2020 }
%0 Conference Paper %1 BaZuHeKoCu-ICDE-Telescope %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 36th International Conference on Data Engineering (ICDE) %D 2020 %T Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field

SPEC Research Group Newsletter, vol. 3 no. 1. S. Kounev; A. van Hoorn; A. Bauer; N. Herbst; (2020, April).
Published by Standard Performance Evaluation Corporation (SPEC)
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@misc{kounev2020research, author = {Kounev, Samuel and van Hoorn, Andre and Bauer, Andr{é} and Herbst, Nikolas}, keywords = {t_other descartes se2}, month = {April}, note = {Published by Standard Performance Evaluation Corporation (SPEC)}, title = {{SPEC Research Group Newsletter, vol. 3 no. 1}}, year = 2020 }
%0 Generic %1 kounev2020research %A Kounev, Samuel %A van Hoorn, Andre %A Bauer, André %A Herbst, Nikolas %D 2020 %T SPEC Research Group Newsletter, vol. 3 no. 1 %U http://research.spec.org/en/newsletter.html

Towards a Self-Aware Prediction of Critical States. M. Züfle; in Organic Computing: Doctoral Dissertation Colloquium 2020, S. Tomforde, C. Krupitzer (Hrsg.) (2020).
(to appear)
[ BibTeX ] [ EndNote ]
 
@incollection{zufle2020towards, author = {Z{ü}fle, Marwin}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {descartes Self-aware-computing statistical_estimation_and_machine_learning prediction online_monitoring_and_forecasting t_bookchapter}, month = {jul}, note = {(to appear)}, title = {Towards a Self-Aware Prediction of Critical States}, year = 2020 }
%0 Book Section %1 zufle2020towards %A Züfle, Marwin %B Organic Computing: Doctoral Dissertation Colloquium 2020 %D 2020 %E Tomforde, Sven %E Krupitzer, Christian %T Towards a Self-Aware Prediction of Critical States

Toward a Framework for Self-Learning Adaptation Planning through Optimization. V. Lesch; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).
[ BibTeX ] [ EndNote ]
 
@incollection{lesch2020toward, author = {Lesch, Veronika}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, keywords = {myown industry_4_0 self-aware-computing optimization t_full descartes intelligent_transportation_systems cyber-physical_systems}, title = {Toward a Framework for Self-Learning Adaptation Planning through Optimization}, year = 2020 }
%0 Book Section %1 lesch2020toward %A Lesch, Veronika %B Organic Computing: Doctoral Dissertation Colloquium 2020 %D 2020 %T Toward a Framework for Self-Learning Adaptation Planning through Optimization

Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press. J. Agne; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, Februar).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Agne2020, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Agne, Joachim}, keywords = {Thesis_supervised_by_SE_member feature_engineering Cyber-Physical_Systems se2_mastersthesis Online_monitoring_and_forecasting descartes Thesis_supervised_by_Marwin_Zuefle Industry_4_0}, month = {February}, school = {University of W{ü}rzburg}, title = {{Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press}}, type = {{MasterThesis}}, year = 2020 }
%0 Thesis %1 Agne2020 %A Agne, Joachim %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press

Energy Efficiency Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite. N. Schmitt; J. Bucek; K.-D. Lange; S. Kounev; in Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@inproceedings{ScLaBuKo-ICPE20-SPEC-CPU2017, address = {New York, NY, USA}, author = {Schmitt, Norbert and Bucek, James and Lange, Klaus-Dieter and Kounev, Samuel}, booktitle = {Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020)}, keywords = {myown Performance descartes SPEC t_wip Power-energy_efficient_computing Power}, month = {April}, publisher = {ACM}, title = {{Energy Efficiency Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite}}, year = 2020 }
%0 Conference Paper %1 ScLaBuKo-ICPE20-SPEC-CPU2017 %A Schmitt, Norbert %A Bucek, James %A Lange, Klaus-Dieter %A Kounev, Samuel %B Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) %C New York, NY, USA %D 2020 %I ACM %T Energy Efficiency Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite %U https://doi.org/10.1145/3375555.3383759

An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. A. Bauer; M. Züfle; J. Grohmann; N. Schmitt; N. Herbst; S. Kounev; in Proceedings of the ACM/SPEC International Conference on Performance Engineering (2020). 48–55.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.
@inproceedings{BaZuGrScHeKo-ICPE20-Seasonal-Forecast, abstract = {Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.}, address = {New York, NY, USA}, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Grohmann, Johannes and Schmitt, Norbert and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the ACM/SPEC International Conference on Performance Engineering}, keywords = {Online_monitoring_and_forecasting t_short descartes Prediction PRISMA}, month = {April}, pages = {48–55}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '20}, title = {{An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series}}, year = 2020 }
%0 Conference Paper %1 BaZuGrScHeKo-ICPE20-Seasonal-Forecast %A Bauer, André %A Züfle, Marwin %A Grohmann, Johannes %A Schmitt, Norbert %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2020 %I Association for Computing Machinery (ACM) %P 48--55 %R 10.1145/3358960.3379123 %T An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series %U https://doi.org/10.1145/3358960.3379123 %X Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy. %@ 9781450369916

A Concept for Crowd-sensed Prediction of Mobile Network Connectivity. S. Herrnleben; B. Zeidler; M. Züfle; C. Krupitzer; S. Kounev; in GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 (2020).
[ BibTeX ] [ EndNote ]
 
@inproceedings{HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity, author = {Herrnleben, Stefan and Zeidler, Bernd and Z{ü}fle, Marwin and Krupitzer, Christian and Kounev, Samuel}, booktitle = {GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020}, keywords = {Internet_of_Things Networking descartes Cloud Prediction Automated_model_learning Application-aware Self-adaptive-systems t_workshop}, month = {February}, title = {{A Concept for Crowd-sensed Prediction of Mobile Network Connectivity}}, year = 2020 }
%0 Conference Paper %1 HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity %A Herrnleben, Stefan %A Zeidler, Bernd %A Züfle, Marwin %A Krupitzer, Christian %A Kounev, Samuel %B GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 %D 2020 %T A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

Model-based Performance Predictions for SDN-based Networks: A Case Study. S. Herrnleben; P. Rygielski; J. Grohmann; S. Eismann; T. Hossfeld; S. Kounev; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Emerging paradigms for network virtualization like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) form new challenges for accurate performance modeling and analysis tools. Therefore, performance modeling and prediction approaches that support SDN or NFV technologies help system operators to analyze the performance of a data center and its corresponding network. The Descartes Network Infrastructures (DNI) offers a high-level descriptive language to model SDN-based networks, which can be transformed into various predictive modeling formalisms. However, these modeling concepts have not yet been evaluated in a realistic scenario. In this paper, we present an extensive case study evaluating the DNI modeling capabilities, the transformations to predictive models, and the performance prediction using the OMNeT++ and SimQPN simulation frameworks. We present five realistic scenarios of a content distribution network (CDN), compare the performance predictions with real-world measurements, and discuss modeling gaps and calibration issues causing mispredictions in some scenarios.
@inproceedings{HeRyGrEiHoKo-MMB2020-Model-based-SDN-Performance, abstract = {Emerging paradigms for network virtualization like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) form new challenges for accurate performance modeling and analysis tools. Therefore, performance modeling and prediction approaches that support SDN or NFV technologies help system operators to analyze the performance of a data center and its corresponding network. The Descartes Network Infrastructures (DNI) offers a high-level descriptive language to model SDN-based networks, which can be transformed into various predictive modeling formalisms. However, these modeling concepts have not yet been evaluated in a realistic scenario. In this paper, we present an extensive case study evaluating the DNI modeling capabilities, the transformations to predictive models, and the performance prediction using the OMNeT++ and SimQPN simulation frameworks. We present five realistic scenarios of a content distribution network (CDN), compare the performance predictions with real-world measurements, and discuss modeling gaps and calibration issues causing mispredictions in some scenarios.}, address = {Cham}, author = {Herrnleben, Stefan and Rygielski, Piotr and Grohmann, Johannes and Eismann, Simon and Hossfeld, Tobias and Kounev, Samuel}, booktitle = {Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems}, keywords = {t_full descartes SDN}, month = {March}, publisher = {Springer}, series = {MMB 2020}, title = {{Model-based Performance Predictions for SDN-based Networks: A Case Study}}, year = 2020 }
%0 Conference Paper %1 HeRyGrEiHoKo-MMB2020-Model-based-SDN-Performance %A Herrnleben, Stefan %A Rygielski, Piotr %A Grohmann, Johannes %A Eismann, Simon %A Hossfeld, Tobias %A Kounev, Samuel %B Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems %C Cham %D 2020 %I Springer %R 10.1007/978-3-030-43024-5_6 %T Model-based Performance Predictions for SDN-based Networks: A Case Study %U https://doi.org/10.1007/978-3-030-43024-5_6 %X Emerging paradigms for network virtualization like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) form new challenges for accurate performance modeling and analysis tools. Therefore, performance modeling and prediction approaches that support SDN or NFV technologies help system operators to analyze the performance of a data center and its corresponding network. The Descartes Network Infrastructures (DNI) offers a high-level descriptive language to model SDN-based networks, which can be transformed into various predictive modeling formalisms. However, these modeling concepts have not yet been evaluated in a realistic scenario. In this paper, we present an extensive case study evaluating the DNI modeling capabilities, the transformations to predictive models, and the performance prediction using the OMNeT++ and SimQPN simulation frameworks. We present five realistic scenarios of a content distribution network (CDN), compare the performance predictions with real-world measurements, and discuss modeling gaps and calibration issues causing mispredictions in some scenarios. %@ 978-3-030-43024-5

Microservices: A Performance Tester’s Dream or Nightmare?. S. Eismann; C.-P. Bezemer; W. Shang; D. Okanovic; A. van Hoorn; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020).
Acceptance Rate: 23.4% (15/64)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.
@inproceedings{EiBeShOkHo2020-ICPE-ServerlessWorkflows, abstract = {In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.}, author = {Eismann, Simon and Bezemer, Cor-Paul and Shang, Weiyi and Okanovic, Dusan and van Hoorn, Andre}, booktitle = {Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {t_full descartes se2}, month = {April}, note = {{Acceptance Rate: 23.4% (15/64)}}, series = {ICPE'20}, title = {{Microservices: A Performance Tester's Dream or Nightmare?}}, year = 2020 }
%0 Conference Paper %1 EiBeShOkHo2020-ICPE-ServerlessWorkflows %A Eismann, Simon %A Bezemer, Cor-Paul %A Shang, Weiyi %A Okanovic, Dusan %A van Hoorn, Andre %B Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) %D 2020 %T Microservices: A Performance Tester's Dream or Nightmare? %U https://dl.acm.org/doi/abs/10.1145/3358960.3379124 %X In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.

Towards Self-Aware Multirotor Formations. D. Kaiser; V. Lesch; J. Rothe; M. Strohmeier; F. Spiess; C. Krupitzer; S. Montenegro; S. Kounev; in Computers (2020). 9(7)
Special Issue on Self-Aware Computing
[ BibTeX ] [ EndNote ] [ Download ]
 
@article{Kaiser2020Towards, author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spiess, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel}, journal = {Computers}, keywords = {Internet_of_Things Cyber-Physical_Systems Design_of_software_and_systems Analytical_and_simulation-based_analysis Simulation descartes Self-aware-computing t_interdisciplinary t_journalmagazine Intelligent_Transportation_Systems}, month = {Februar}, note = {Special Issue on Self-Aware Computing}, number = 7, publisher = {MDPI}, title = {{Towards Self-Aware Multirotor Formations}}, volume = 9, year = 2020 }
%0 Journal Article %1 Kaiser2020Towards %A Kaiser, Dennis %A Lesch, Veronika %A Rothe, Julian %A Strohmeier, Michael %A Spiess, Florian %A Krupitzer, Christian %A Montenegro, Sergio %A Kounev, Samuel %D 2020 %I MDPI %J Computers %N 7 %T Towards Self-Aware Multirotor Formations %V 9

A Survey on Human Machine Interaction in Industry 4.0 C. Krupitzer; S. Müller; V. Lesch; M. Züfle; J. Edinger; A. Lemken; D. Schäfer; S. Kounev; C. Becker; (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@techreport{Krupitzer2020ASurvey, author = {Krupitzer, Christian and M{ü}ller, Sebastian and Lesch, Veronika and Z{ü}fle, Marwin and Edinger, Janick and Lemken, Alexander and Sch{ä}fer, Dominik and Kounev, Samuel and Becker, Christian}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and ioxp GmbH and Syntax Systems GmbH}, journal = {arXiv}, keywords = {Survey descartes Industry_4_0 t_techreport t_interdisciplinary}, month = {feb}, title = {{A Survey on Human Machine Interaction in Industry 4.0}}, year = 2020 }
%0 Report %1 Krupitzer2020ASurvey %A Krupitzer, Christian %A Müller, Sebastian %A Lesch, Veronika %A Züfle, Marwin %A Edinger, Janick %A Lemken, Alexander %A Schäfer, Dominik %A Kounev, Samuel %A Becker, Christian %D 2020 %J arXiv %T A Survey on Human Machine Interaction in Industry 4.0 %U https://arxiv.org/abs/2002.01025

To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows. M. Züfle; C. Krupitzer; F. Erhard; J. Grohmann; S. Kounev; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020). 19–36.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.
@inproceedings{ZuKrErGrKo-MMB2020-ToFailOrNotToFail, abstract = {Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.}, address = {Cham}, author = {Z{ü}fle, Marwin and Krupitzer, Christian and Erhard, Florian and Grohmann, Johannes and Kounev, Samuel}, booktitle = {Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems}, keywords = {Statistical_estimation_and_machine_learning Internet_of_Things Cyber-Physical_Systems Performance t_full descartes Prediction PRISMA Reliability}, month = {March}, pages = {19–36}, publisher = {Springer}, series = {MMB 2020}, title = {{To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows}}, year = 2020 }
%0 Conference Paper %1 ZuKrErGrKo-MMB2020-ToFailOrNotToFail %A Züfle, Marwin %A Krupitzer, Christian %A Erhard, Florian %A Grohmann, Johannes %A Kounev, Samuel %B Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems %C Cham %D 2020 %I Springer %P 19--36 %R 10.1007/978-3-030-43024-5_2 %T To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows %U https://doi.org/10.1007/978-3-030-43024-5_2 %X Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure. %@ 978-3-030-43024-5

Time Series Forecasting for Self-Aware Systems. A. Bauer; M. Züfle; N. Herbst; A. Zehe; A. Hotho; S. Kounev; in Proceedings of the IEEE (2020). 108(7) 1068–1093.
[ BibTeX ] [ EndNote ] [ DOI ]
 
@article{BaZuHeZeHoKo-PIEEE-Forecasting, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Zehe, Albin and Hotho, Andreas and Kounev, Samuel}, journal = {Proceedings of the IEEE}, keywords = {Telescope Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting descartes Self-aware-computing Prediction Elasticity t_journalmagazine}, month = 7, number = 7, pages = {1068 - 1093}, title = {{Time Series Forecasting for Self-Aware Systems}}, volume = 108, year = 2020 }
%0 Journal Article %1 BaZuHeZeHoKo-PIEEE-Forecasting %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Zehe, Albin %A Hotho, Andreas %A Kounev, Samuel %D 2020 %J Proceedings of the IEEE %N 7 %P 1068 - 1093 %R 10.1109/JPROC.2020.2983857 %T Time Series Forecasting for Self-Aware Systems %V 108

Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation’’. C. Krupitzer; B. Eberhardinger; I. Gerostathopoulos; C. Raibulet; (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umea, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.
@misc{KrEbGeRa2020_Computers_seacSI, abstract = {The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umea, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.}, author = {Krupitzer, Christian and Eberhardinger, Benedikt and Gerostathopoulos, Ilias and Raibulet, Claudia}, journal = {MDPI Computers}, keywords = {descartes se2}, number = 1, publisher = {MDPI}, title = {{Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation''}}, volume = 9, year = 2020 }
%0 Generic %1 KrEbGeRa2020_Computers_seacSI %A Krupitzer, Christian %A Eberhardinger, Benedikt %A Gerostathopoulos, Ilias %A Raibulet, Claudia %D 2020 %I MDPI %J MDPI Computers %N 1 %T Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation'' %U https://www.mdpi.com/2073-431X/9/1/22 %V 9 %X The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umea, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.

Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. L. Abad; J. N. Amaral; P. Tuma; A. Iosup; in Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria, M. Felderer, W. Hasselbring, R. Rabiser, R. Jung (Hrsg.) (2020). (Bd. P-300) 93–94.
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@inproceedings{DBLP:conf/se/PapadopoulosVBH20, author = {Papadopoulos, Alessandro Vittorio and Versluis, Laurens and Bauer, Andr{{é}} and Herbst, Nikolas and von Kistowski, J{{ó}}akim and Ali{-}Eldin, Ahmed and Abad, Cristina L. and Amaral, Jos{{é}} Nelson and Tuma, Petr and Iosup, Alexandru}, booktitle = {Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria}, editor = {Felderer, Michael and Hasselbring, Wilhelm and Rabiser, Rick and Jung, Reiner}, keywords = {t_extendedabstract descartes se2}, pages = {93–94}, publisher = {Gesellschaft f{{ü}}r Informatik e.V.}, series = {{LNI}}, title = {{Methodological Principles for Reproducible Performance Evaluation in Cloud Computing}}, volume = {{P-300}}, year = 2020 }
%0 Conference Paper %1 DBLP:conf/se/PapadopoulosVBH20 %A Papadopoulos, Alessandro Vittorio %A Versluis, Laurens %A Bauer, André %A Herbst, Nikolas %A von Kistowski, Jóakim %A Ali-Eldin, Ahmed %A Abad, Cristina L. %A Amaral, José Nelson %A Tuma, Petr %A Iosup, Alexandru %B Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria %D 2020 %E Felderer, Michael %E Hasselbring, Wilhelm %E Rabiser, Rick %E Jung, Reiner %I Gesellschaft für Informatik e.V. %P 93--94 %T Methodological Principles for Reproducible Performance Evaluation in Cloud Computing %U https://doi.org/10.18420/SE2020_27 %V P-300

A Survey on Predictive Maintenance for Industry 4.0 C. Krupitzer; T. Wagenhals; M. Züfle; V. Lesch; D. Schäfer; A. Mozaffarin; J. Edinger; C. Becker; S. Kounev; (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@techreport{Krupitzer2020ASurveyPdM, author = {Krupitzer, Christian and Wagenhals, Tim and Z{ü}fle, Marwin and Lesch, Veronika and Sch{ä}fer, Dominik and Mozaffarin, Amin and Edinger, Janick and Becker, Christian and Kounev, Samuel}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and Syntax Systems GmbH and MOZYS Engineering GmbH}, journal = {arXiv}, keywords = {Internet_of_Things Survey Cyber-Physical_Systems Online_monitoring_and_forecasting descartes Prediction Industry_4_0 t_techreport t_interdisciplinary Reliability}, month = {feb}, title = {{A Survey on Predictive Maintenance for Industry 4.0}}, year = 2020 }
%0 Report %1 Krupitzer2020ASurveyPdM %A Krupitzer, Christian %A Wagenhals, Tim %A Züfle, Marwin %A Lesch, Veronika %A Schäfer, Dominik %A Mozaffarin, Amin %A Edinger, Janick %A Becker, Christian %A Kounev, Samuel %D 2020 %J arXiv %T A Survey on Predictive Maintenance for Industry 4.0 %U https://arxiv.org/abs/2002.08224

Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study. P. Düking; H.-C. Holmberg; P. Kunz; R. Leppich; B. Sperlich; in European Journal of Applied Physiology (2020). 1–9.
[ BibTeX ] [ EndNote ]
 
@article{duking2020intra, author = {D{ü}king, Peter and Holmberg, Hans-Christer and Kunz, Philipp and Leppich, Robert and Sperlich, Billy}, journal = {European Journal of Applied Physiology}, keywords = {myown descartes sport t_interdisciplinary t_journalmagazine}, pages = {1–9}, publisher = {Springer}, title = {Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study}, year = 2020 }
%0 Journal Article %1 duking2020intra %A Düking, Peter %A Holmberg, Hans-Christer %A Kunz, Philipp %A Leppich, Robert %A Sperlich, Billy %D 2020 %I Springer %J European Journal of Applied Physiology %P 1--9 %T Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study

2019 [ nach oben ]

A Modular Simulation Framework for Analyzing Platooning Coordination. C. Krupitzer; V. Lesch; M. Pfannemüller; C. Becker; M. Segata; in Proceedings of the 1st ACM Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019 (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{KrLePfBeSe2019_TOPCars, author = {Krupitzer, Christian and Lesch, Veronika and Pfannem{ü}ller, Martin and Becker, Christian and Segata, Michele}, booktitle = {Proceedings of the 1st {ACM} Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019}, keywords = {Tool Internet_of_Things Cyber-Physical_Systems Analytical_and_simulation-based_analysis Simulation descartes t_workshop Intelligent_Transportation_Systems}, month = {July}, publisher = {ACM}, title = {{A Modular Simulation Framework for Analyzing Platooning Coordination}}, year = 2019 }
%0 Conference Paper %1 KrLePfBeSe2019_TOPCars %A Krupitzer, Christian %A Lesch, Veronika %A Pfannemüller, Martin %A Becker, Christian %A Segata, Michele %B Proceedings of the 1st ACM Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019 %D 2019 %I ACM %T A Modular Simulation Framework for Analyzing Platooning Coordination

Agile Scalability Engineering: The ScrumScale Method. G. Brataas; G. Hanssen; N. Herbst; A. van Hoorn; in IEEE Software (2019). 37(5) 77–85.
to appear
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ]
 
Scalability is a property that must be carefully designed into a system. A case study in the largest Norwegian public portal, Altinn, illustrates how developers and scalability experts improved scalability and spent less time during scalability testing. With minor adjustments to an agile development process, stakeholders spend more up-front time together.
@article{ScrumScale2019IEEESW, abstract = {Scalability is a property that must be carefully designed into a system. A case study in the largest Norwegian public portal, Altinn, illustrates how developers and scalability experts improved scalability and spent less time during scalability testing. With minor adjustments to an agile development process, stakeholders spend more up-front time together.}, author = {Brataas, Gunnar and Hanssen, Geir and Herbst, Nikolas and van Hoorn, Andr{é}}, journal = {IEEE Software}, keywords = {myown descartes t_journalmagazine sys:relevantfor:se-group se2}, month = {June}, note = {to appear}, number = 5, pages = {77-85}, publisher = {IEEE}, title = {Agile Scalability Engineering: The ScrumScale Method}, volume = 37, year = 2019 }
%0 Journal Article %1 ScrumScale2019IEEESW %A Brataas, Gunnar %A Hanssen, Geir %A Herbst, Nikolas %A van Hoorn, André %D 2019 %I IEEE %J IEEE Software %N 5 %P 77-85 %R 10.1109/MS.2019.2923184 %T Agile Scalability Engineering: The ScrumScale Method %U https://ieeexplore.ieee.org/document/8736723 %V 37 %X Scalability is a property that must be carefully designed into a system. A case study in the largest Norwegian public portal, Altinn, illustrates how developers and scalability experts improved scalability and spent less time during scalability testing. With minor adjustments to an agile development process, stakeholders spend more up-front time together.

Online Power Consumption Estimation for Functions in Cloud Applications. N. Schmitt; L. Iffländer; A. Bauer; S. Kounev; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{ScIfBaKo2019-ICAC-OnlinePowerEstimation, author = {Schmitt, Norbert and Iffländer, Lukas and Bauer, André and Kounev, Samuel}, booktitle = {Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC)}, keywords = {myown t_full descartes Cloud Power-energy_efficient_computing Power}, month = {June}, publisher = {IEEE}, title = {Online Power Consumption Estimation for Functions in Cloud Applications}, year = 2019 }
%0 Conference Paper %1 ScIfBaKo2019-ICAC-OnlinePowerEstimation %A Schmitt, Norbert %A Iffländer, Lukas %A Bauer, André %A Kounev, Samuel %B Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) %D 2019 %I IEEE %T Online Power Consumption Estimation for Functions in Cloud Applications

Utilizing Clustering to Optimize Resource Demand Estimation Approaches. J. Grohmann; S. Eismann; A. Bauer; M. Zuefle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134–139.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.
@inproceedings{GrEiBaZuHeKo2019-ICAC-RDEClustering, abstract = {Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.}, author = {Grohmann, Johannes and Eismann, Simon and Bauer, Andre and Zuefle, Marwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)}, keywords = {Statistical_estimation_and_machine_learning Performance descartes Self-aware-computing PRISMA GoogleResearchAward LibReDE Instrumentation_profiling_and_workload_characterization Self-adaptive-systems t_workshop Optimization}, month = {June}, pages = {134–139}, publisher = {IEEE}, title = {{Utilizing Clustering to Optimize Resource Demand Estimation Approaches}}, year = 2019 }
%0 Conference Paper %1 GrEiBaZuHeKo2019-ICAC-RDEClustering %A Grohmann, Johannes %A Eismann, Simon %A Bauer, Andre %A Zuefle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) %D 2019 %I IEEE %P 134--139 %R 10.1109/FAS-W.2019.00043 %T Utilizing Clustering to Optimize Resource Demand Estimation Approaches %U https://doi.org/10.1109/FAS-W.2019.00043 %X Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.

Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge. S. Kounev; (2019, Juni).
[ BibTeX ] [ EndNote ]
 
@misc{Talk-Ko2019-RotaryClub-SeAC, address = {W{ü}rzburz, Germany}, author = {Kounev, Samuel}, howpublished = {Rotary Club W{ü}rzburg, 11.06.2019}, keywords = {se2_talk t_talk descartes}, month = {June}, title = {{Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge}}, year = 2019 }
%0 Generic %1 Talk-Ko2019-RotaryClub-SeAC %A Kounev, Samuel %C Würzburz, Germany %D 2019 %T Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge

From the Internet-of-Things to the Internet-of-Thinking-Things. S. Kounev; (2019, Juli).
[ BibTeX ] [ EndNote ]
 
@misc{talk-Kounev-InvTUM19, author = {Kounev, Samuel}, howpublished = {Informatik-Sonderkolloquium "Engineering Software for Decentralized Systems", Fakult{ä}t f{ü}r Informatik, Technische Universit{ä}t M{ü}nchen, Munich, Germany}, keywords = {se2_talk t_talk descartes}, month = {Jul}, title = {{From the Internet-of-Things to the Internet-of-Thinking-Things}}, year = 2019 }
%0 Generic %1 talk-Kounev-InvTUM19 %A Kounev, Samuel %D 2019 %T From the Internet-of-Things to the Internet-of-Thinking-Things

Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen. J. Stoll; L. Iffländer; (2019, April).
[ BibTeX ] [ EndNote ]
 
@misc{talk-StIf-SDN-buildingIoT, author = {Stoll, Jonathan and Iffländer, Lukas}, howpublished = {building IoT 2019}, keywords = {myown se2_talk t_talk descartes}, month = {04}, title = {Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen}, year = 2019 }
%0 Generic %1 talk-StIf-SDN-buildingIoT %A Stoll, Jonathan %A Iffländer, Lukas %D 2019 %T Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen

Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques. J. Grohmann; S. Eismann; S. Elflein; M. Mazkatli; J. von Kistowski; S. Kounev; in 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2019). 309–322.
Acceptance Rate: 23.8% (29/122)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard.
@inproceedings{GrEiElMaKiKo2019-MASCOTS-DependencyIdentification, abstract = {Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard.}, author = {Grohmann, Johannes and Eismann, Simon and Elflein, Sven and Mazkatli, Manar and von Kistowski, J{ó}akim and Kounev, Samuel}, booktitle = {2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {Statistical_estimation_and_machine_learning DML Performance t_full descartes Self-aware-computing Prediction PRISMA Automated_model_learning}, month = {October}, note = {{Acceptance Rate: 23.8% (29/122)}}, pages = {309–322}, publisher = {IEEE Computer Society}, series = {MASCOTS '19}, title = {{Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques}}, year = 2019 }
%0 Conference Paper %1 GrEiElMaKiKo2019-MASCOTS-DependencyIdentification %A Grohmann, Johannes %A Eismann, Simon %A Elflein, Sven %A Mazkatli, Manar %A von Kistowski, Jóakim %A Kounev, Samuel %B 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) %D 2019 %I IEEE Computer Society %P 309--322 %R 10.1109/MASCOTS.2019.00042 %T Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques %U https://doi.org/10.1109/MASCOTS.2019.00042 %X Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard. %@ 978-1-7281-4950-9

Towards Testing the Software Aging Behavior of Hypervisor Hypercall Interfaces. L. Beierlieb; L. Iffländer; A. Milenkoski; C. F. Goncalves; N. Antunes; S. Kounev; in 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. As long-running applications, they are susceptible to software aging. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research either deals with the aging properties of hypervisors in general without considering the hypercalls or focuses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports extensive test campaigns as well as the modeling of hypercall interfaces.
@inproceedings{Beierlieb_2019_TowardsTestingtheSoftwareAgingBehaviorofHypervisorHypercallInterfaces, abstract = {With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. As long-running applications, they are susceptible to software aging. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research either deals with the aging properties of hypervisors in general without considering the hypercalls or focuses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports extensive test campaigns as well as the modeling of hypercall interfaces.}, author = {Beierlieb, Lukas and Iffländer, Lukas and Milenkoski, Aleksandar and Goncalves, Charles F. and Antunes, Nuno and Kounev, Samuel}, booktitle = {2019 {IEEE} International Symposium on Software Reliability Engineering Workshops ({ISSREW})}, keywords = {myown Multi-tenancy Performance descartes Instrumentation_profiling_and_workload_characterization Security Cloud EvIDencE HInjector Isolation Virtualization Reliability t_workshop}, month = 11, organization = {{IEEE}}, title = {Towards Testing the Software Aging Behavior of Hypervisor Hypercall Interfaces}, year = 2019 }
%0 Conference Paper %1 Beierlieb_2019_TowardsTestingtheSoftwareAgingBehaviorofHypervisorHypercallInterfaces %A Beierlieb, Lukas %A Iffländer, Lukas %A Milenkoski, Aleksandar %A Goncalves, Charles F. %A Antunes, Nuno %A Kounev, Samuel %B 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) %D 2019 %T Towards Testing the Software Aging Behavior of Hypervisor Hypercall Interfaces %X With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. As long-running applications, they are susceptible to software aging. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research either deals with the aging properties of hypervisors in general without considering the hypercalls or focuses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports extensive test campaigns as well as the modeling of hypercall interfaces.

On Learning Parametric Dependencies from Monitoring Data. J. Grohmann; S. Eismann; S. Kounev; in Proceedings of the 10th Symposium on Software Performance 2019 (SSP’19) (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
A common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them.
@inproceedings{GrEiKo2019-SSP-LearningDependencies, abstract = {A common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them.}, author = {Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, booktitle = {Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19)}, keywords = {Statistical_estimation_and_machine_learning Performance descartes Self-aware-computing Prediction Automated_model_learning Self-adaptive-systems t_workshop}, month = {November}, title = {{On Learning Parametric Dependencies from Monitoring Data}}, year = 2019 }
%0 Conference Paper %1 GrEiKo2019-SSP-LearningDependencies %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %B Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19) %D 2019 %T On Learning Parametric Dependencies from Monitoring Data %U https://pi.informatik.uni-siegen.de/stt/39_4/01_Fachgruppenberichte/SSP2019/SSP2019_Grohmann.pdf %X A common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them.

Towards Testing the Performance Influence of Hypervisor Hypercall Interface Behavior. L. Beierlieb; L. Iffländer; S. Kounev; A. Milenkoski; in Proceedings of the 10th Symposium on Software Performance 2019 (SSP’19) (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research focusses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports test campaigns and modeling of hypercall interfaces.
@inproceedings{Beierlieb_2019_TowardsTestingthePerformanceInfluenceofHypervisorHypercallInterfaceBehavior, abstract = {With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research focusses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports test campaigns and modeling of hypercall interfaces.}, author = {Beierlieb, Lukas and Iffländer, Lukas and Kounev, Samuel and Milenkoski, Aleksandar}, booktitle = {Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19)}, keywords = {myown Performance Security descartes EvIDencE HInjector Isolation Instrumentation_profiling_and_workload_characterization Virtualization t_workshop}, month = 11, title = {Towards Testing the Performance Influence of Hypervisor Hypercall Interface Behavior}, year = 2019 }
%0 Conference Paper %1 Beierlieb_2019_TowardsTestingthePerformanceInfluenceofHypervisorHypercallInterfaceBehavior %A Beierlieb, Lukas %A Iffländer, Lukas %A Kounev, Samuel %A Milenkoski, Aleksandar %B Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19) %D 2019 %T Towards Testing the Performance Influence of Hypervisor Hypercall Interface Behavior %X With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough robustness to assure performance, security, and reliability. Existing research focusses on finding hypercall-related vulnerabilities. In this work, we discuss open challenges regarding hypercall interfaces. To address these challenges, we propose an extensive framework architecture to perform robustness testing on hypercall interfaces. This framework supports test campaigns and modeling of hypercall interfaces.

Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning. J. Grohmann; P. K. Nicholson; J. O. Iglesias; S. Kounev; D. Lugones; in Proceedings of the 20th International Middleware Conference (2019). 149–162.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Today, software operation engineers rely on application key performance indicators (KPIs) for sizing and orchestrating cloud resources dynamically. KPIs are monitored to assess the achievable performance and to configure various cloud-specific parameters such as flavors of instances and autoscaling rules, among others. Usually, keeping KPIs within acceptable levels requires application expertise which is expensive and can slow down the continuous delivery of software. Expertise is required because KPIs are normally based on application-specific quality-of-service metrics, like service response time and processing rate, instead of generic platform metrics, like those typical across various environments (e.g., CPU and memory utilization, I/O rate, etc.)In this paper, we investigate the feasibility of outsourcing the management of application performance from developers to cloud operators. In the same way that the serverless paradigm allows the execution environment to be fully managed by a third party, we discuss a monitorless model to streamline application deployment by delegating performance management. We show that training a machine learning model with platform-level data, collected from the execution of representative containerized services, allows inferring application KPI degradation. This is an opportunity to simplify operations as engineers can rely solely on platform metrics -- while still fulfilling application KPIs -- to configure portable and application agnostic rules and other cloud-specific parameters to automatically trigger actions such as autoscaling, instance migration, network slicing, etc.Results show that monitorless infers KPI degradation with an accuracy of 97% and, notably, it performs similarly to typical autoscaling solutions, even when autoscaling rules are optimally tuned with knowledge of the expected workload.
@inproceedings{GrNiIgKoLu-MIDDLEWARE2019-Monitorless, abstract = {Today, software operation engineers rely on application key performance indicators (KPIs) for sizing and orchestrating cloud resources dynamically. KPIs are monitored to assess the achievable performance and to configure various cloud-specific parameters such as flavors of instances and autoscaling rules, among others. Usually, keeping KPIs within acceptable levels requires application expertise which is expensive and can slow down the continuous delivery of software. Expertise is required because KPIs are normally based on application-specific quality-of-service metrics, like service response time and processing rate, instead of generic platform metrics, like those typical across various environments (e.g., CPU and memory utilization, I/O rate, etc.)In this paper, we investigate the feasibility of outsourcing the management of application performance from developers to cloud operators. In the same way that the serverless paradigm allows the execution environment to be fully managed by a third party, we discuss a monitorless model to streamline application deployment by delegating performance management. We show that training a machine learning model with platform-level data, collected from the execution of representative containerized services, allows inferring application KPI degradation. This is an opportunity to simplify operations as engineers can rely solely on platform metrics – while still fulfilling application KPIs – to configure portable and application agnostic rules and other cloud-specific parameters to automatically trigger actions such as autoscaling, instance migration, network slicing, etc.Results show that monitorless infers KPI degradation with an accuracy of 97% and, notably, it performs similarly to typical autoscaling solutions, even when autoscaling rules are optimally tuned with knowledge of the expected workload.}, address = {New York, NY, USA}, author = {Grohmann, Johannes and Nicholson, Patrick K. and Iglesias, Jesus Omana and Kounev, Samuel and Lugones, Diego}, booktitle = {Proceedings of the 20th International Middleware Conference}, keywords = {Statistical_estimation_and_machine_learning Multi-tenancy Performance t_full descartes Cloud Prediction Application_quality_of_service_management Instrumentation_profiling_and_workload_characterization Virtualization}, pages = {149–162}, publisher = {Association for Computing Machinery (ACM)}, series = {Middleware '19}, title = {Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning}, year = 2019 }
%0 Conference Paper %1 GrNiIgKoLu-MIDDLEWARE2019-Monitorless %A Grohmann, Johannes %A Nicholson, Patrick K. %A Iglesias, Jesus Omana %A Kounev, Samuel %A Lugones, Diego %B Proceedings of the 20th International Middleware Conference %C New York, NY, USA %D 2019 %I Association for Computing Machinery (ACM) %P 149--162 %R 10.1145/3361525.3361543 %T Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning %U https://doi.org/10.1145/3361525.3361543 %X Today, software operation engineers rely on application key performance indicators (KPIs) for sizing and orchestrating cloud resources dynamically. KPIs are monitored to assess the achievable performance and to configure various cloud-specific parameters such as flavors of instances and autoscaling rules, among others. Usually, keeping KPIs within acceptable levels requires application expertise which is expensive and can slow down the continuous delivery of software. Expertise is required because KPIs are normally based on application-specific quality-of-service metrics, like service response time and processing rate, instead of generic platform metrics, like those typical across various environments (e.g., CPU and memory utilization, I/O rate, etc.)In this paper, we investigate the feasibility of outsourcing the management of application performance from developers to cloud operators. In the same way that the serverless paradigm allows the execution environment to be fully managed by a third party, we discuss a monitorless model to streamline application deployment by delegating performance management. We show that training a machine learning model with platform-level data, collected from the execution of representative containerized services, allows inferring application KPI degradation. This is an opportunity to simplify operations as engineers can rely solely on platform metrics -- while still fulfilling application KPIs -- to configure portable and application agnostic rules and other cloud-specific parameters to automatically trigger actions such as autoscaling, instance migration, network slicing, etc.Results show that monitorless infers KPI degradation with an accuracy of 97% and, notably, it performs similarly to typical autoscaling solutions, even when autoscaling rules are optimally tuned with knowledge of the expected workload. %@ 9781450370097

How is Performance Addressed in DevOps?. C.-P. Bezemer; S. Eismann; V. Ferme; J. Grohmann; R. Heinrich; P. Jamshidi; W. Shang; A. van Hoorn; M. Villavicencio; J. Walter; F. Willnecker; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45–50.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities --- such as load testing --- require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners.
@inproceedings{BeEiFeGrRhJaShHoViWaWi2019-ICPE-DevOpsSurvey, abstract = {DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities — such as load testing — require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners.}, address = {New York, NY, USA}, author = {Bezemer, Cor{-}Paul and Eismann, Simon and Ferme, Vincenzo and Grohmann, Johannes and Heinrich, Robert and Jamshidi, Pooyan and Shang, Weiyi and van Hoorn, Andr{{é}} and Villavicencio, M{{ó}}nica and Walter, J{{ü}}rgen and Willnecker, Felix}, booktitle = {Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering}, keywords = {Survey Performance Design_of_software_and_systems t_short descartes Instrumentation_profiling_and_workload_characterization SPEC}, pages = {45–50}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '19}, title = {{How is Performance Addressed in DevOps?}}, year = 2019 }
%0 Conference Paper %1 BeEiFeGrRhJaShHoViWaWi2019-ICPE-DevOpsSurvey %A Bezemer, Cor-Paul %A Eismann, Simon %A Ferme, Vincenzo %A Grohmann, Johannes %A Heinrich, Robert %A Jamshidi, Pooyan %A Shang, Weiyi %A van Hoorn, André %A Villavicencio, Mónica %A Walter, Jürgen %A Willnecker, Felix %B Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I Association for Computing Machinery (ACM) %P 45--50 %R 10.1145/3297663.3309672 %T How is Performance Addressed in DevOps? %U https://doi.org/10.1145/3297663.3309672 %X DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities --- such as load testing --- require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners. %@ 9781450362399

Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. Abad; J. N. Amaral; P. Tuma; A. Iosup; in IEEE Transactions on Software Engineering (2019). 47(8) 1528–1543.
[ BibTeX ] [ EndNote ] [ DOI ] [ Download ]
 
@article{PaVeBaHeKiAEAbAmTuIo2019-TSE-MethodologicalPrinciples, author = {Papadopoulos, Alessandro Vittorio and Versluis, Laurens and Bauer, Andr{é} and Herbst, Nikolas and von Kistowski, J{ó}akim and Ali-Eldin, Ahmed and Abad, Cristina and Amaral, J. Nelson and Tuma, Petr and Iosup, Alexandru}, journal = {IEEE Transactions on Software Engineering}, keywords = {Metrics_and_benchmarking_methodologies Performance descartes Cloud t_journalmagazine SPEC}, month = 7, number = 8, pages = {1528-1543}, title = {Methodological Principles for Reproducible Performance Evaluation in Cloud Computing}, volume = 47, year = 2019 }
%0 Journal Article %1 PaVeBaHeKiAEAbAmTuIo2019-TSE-MethodologicalPrinciples %A Papadopoulos, Alessandro Vittorio %A Versluis, Laurens %A Bauer, André %A Herbst, Nikolas %A von Kistowski, Jóakim %A Ali-Eldin, Ahmed %A Abad, Cristina %A Amaral, J. Nelson %A Tuma, Petr %A Iosup, Alexandru %D 2019 %J IEEE Transactions on Software Engineering %N 8 %P 1528-1543 %R 10.1109/TSE.2019.2927908 %T Methodological Principles for Reproducible Performance Evaluation in Cloud Computing %V 47

Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. N. Herbst; (2019, Juni).
[ BibTeX ] [ EndNote ] [ Download ]
 
@misc{talk-Herbst-GIAwardNomination-Dagstuhl, author = {Herbst, Nikolas}, howpublished = {GI Dissertationspreis: Seminar f{ü}r Nominierte Doktoranden, Dagstuhl, Germany, June 28, 2019}, keywords = {Telescope Metrics_and_benchmarking_methodologies Resource_management descartes Prediction Chameleon Instrumentation_profiling_and_workload_characterization LIMBO Online_monitoring_and_forecasting se2_talk Cloud Award BUNGEE Virtualization t_bookchapter}, month = {June}, title = {{Methoden und Messverfahren f{ü}r Automatisches Skalieren in Elastischen Cloud Umgebungen}}, year = 2019 }
%0 Generic %1 talk-Herbst-GIAwardNomination-Dagstuhl %A Herbst, Nikolas %D 2019 %T Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen

Systematic Search for Optimal Resource Configurations of Distributed Applications. A. Bauer; S. Eismann; J. Grohmann; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 120–125.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.
@inproceedings{BaEiGrHeKo-ICAC-BUNGEE, abstract = {With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.}, address = {Los Alamitos, CA, USA}, author = {Bauer, Andr{é} and Eismann, Simon and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, booktitle = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)}, keywords = {Resource_management descartes Cloud Elasticity BUNGEE Multi-criteria_optimization t_workshop Optimization}, month = {jun}, pages = {120–125}, publisher = {IEEE Computer Society}, title = {{Systematic Search for Optimal Resource Configurations of Distributed Applications}}, year = 2019 }
%0 Conference Paper %1 BaEiGrHeKo-ICAC-BUNGEE %A Bauer, André %A Eismann, Simon %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) %C Los Alamitos, CA, USA %D 2019 %I IEEE Computer Society %P 120--125 %R 10.1109/FAS-W.2019.00040 %T Systematic Search for Optimal Resource Configurations of Distributed Applications %U https://doi.ieeecomputersociety.org/10.1109/FAS-W.2019.00040 %X With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.

Kaa: Evaluating Elasticity of Cloud-hosted DBMS. D. Seybold; S. Volpert; S. Wesner; A. Bauer; N. Herbst; J. Domaschka; in Proceedings of the 11th IEEE International Conference on Cloud Computing (CloudCom) (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{SeVoWeBaHeDo-CloudCom-Kaa, author = {Seybold, Daniel and Volpert, Simon and Wesner, Stefan and Bauer, Andr{é} and Herbst, Nikolas and Domaschka, J{ö}rg}, booktitle = {Proceedings of the 11th IEEE International Conference on Cloud Computing (CloudCom)}, keywords = {Tool Metrics_and_benchmarking_methodologies Performance t_full descartes Cloud Elasticity}, month = {December}, title = {Kaa: Evaluating Elasticity of Cloud-hosted DBMS}, year = 2019 }
%0 Conference Paper %1 SeVoWeBaHeDo-CloudCom-Kaa %A Seybold, Daniel %A Volpert, Simon %A Wesner, Stefan %A Bauer, André %A Herbst, Nikolas %A Domaschka, Jörg %B Proceedings of the 11th IEEE International Conference on Cloud Computing (CloudCom) %D 2019 %T Kaa: Evaluating Elasticity of Cloud-hosted DBMS

TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research. S. Eismann; (2019, Februar).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed micro-service applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customization and should use a modern and representative technology stack. Unfortunately, existing testing and benchmarking applications are either outdated, designed for specific testing scenarios, or do not offer the necessary degrees of freedom. In this talk, we introduce the TeaStore, a state-of-the-art test and reference application with a micro-service architecture. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. Based on three experiments, we demonstrate TeaStore's use in three different research contexts: performance modeling, cloud resource management, and energy efficiency analysis.
@misc{talk-Eismann-SE2019, abstract = {Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed micro-service applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customization and should use a modern and representative technology stack. Unfortunately, existing testing and benchmarking applications are either outdated, designed for specific testing scenarios, or do not offer the necessary degrees of freedom. In this talk, we introduce the TeaStore, a state-of-the-art test and reference application with a micro-service architecture. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. Based on three experiments, we demonstrate TeaStore's use in three different research contexts: performance modeling, cloud resource management, and energy efficiency analysis.}, author = {Eismann, Simon}, howpublished = {Multikonferenz Software Engineering & Management 2019}, keywords = {Tool Design_of_software_and_systems se2_talk t_talk SOA descartes Cloud}, month = {February}, title = {{TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research}}, year = 2019 }
%0 Generic %1 talk-Eismann-SE2019 %A Eismann, Simon %D 2019 %T TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research %X Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed micro-service applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customization and should use a modern and representative technology stack. Unfortunately, existing testing and benchmarking applications are either outdated, designed for specific testing scenarios, or do not offer the necessary degrees of freedom. In this talk, we introduce the TeaStore, a state-of-the-art test and reference application with a micro-service architecture. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. Based on three experiments, we demonstrate TeaStore's use in three different research contexts: performance modeling, cloud resource management, and energy efficiency analysis.

Performance Influence of Security Function Chain Ordering. L. Iffländer; N. Fella; in Companion of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45–46.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
In modern days security systems often reach their performance peak and limit the protected application. Utilizing the available resources for security more efficiently is becoming more critical. In this paper, we introduce the claim, that no static security function chain is optimal in every situation. First experiments prove our claim.
@inproceedings{Ifflaender2019_PerformanceInfluenceofSecurityFunctionChainOrdering, abstract = {In modern days security systems often reach their performance peak and limit the protected application. Utilizing the available resources for security more efficiently is becoming more critical. In this paper, we introduce the claim, that no static security function chain is optimal in every situation. First experiments prove our claim.}, address = {New York, NY, USA}, author = {Iffländer, Lukas and Fella, Nicolas}, booktitle = {Companion of the 2019 ACM/SPEC International Conference on Performance Engineering}, keywords = {Metrics_and_benchmarking_methodologies Resource_management Networking Performance descartes Optimization Security Online_monitoring_and_forecasting Self-aware-computing EvIDencE Application-aware SDN t_poster Reliability Self-adaptive-systems}, pages = {45–46}, publisher = {ACM}, series = {ICPE '19}, title = {Performance Influence of Security Function Chain Ordering}, year = 2019 }
%0 Conference Paper %1 Ifflaender2019_PerformanceInfluenceofSecurityFunctionChainOrdering %A Iffländer, Lukas %A Fella, Nicolas %B Companion of the 2019 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I ACM %P 45--46 %T Performance Influence of Security Function Chain Ordering %U http://doi.acm.org/10.1145/3302541.3311965 %X In modern days security systems often reach their performance peak and limit the protected application. Utilizing the available resources for security more efficiently is becoming more critical. In this paper, we introduce the claim, that no static security function chain is optimal in every situation. First experiments prove our claim.

Integrating Statistical Response Time Models in Architectural Performance Models. S. Eismann; J. Grohmann; J. Walter; J. von Kistowski; S. Kounev; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80.
Acceptance Rate: 21,9% (21/96)
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ DOI ] [ Download ]
 
Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.
@inproceedings{EiGrWaKiKo2019-ICSA-Integrating, abstract = {Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.}, author = {Eismann, Simon and Grohmann, Johannes and Walter, J{ü}rgen and von Kistowski, J{ó}akim and Kounev, Samuel}, booktitle = {Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA)}, keywords = {Statistical_estimation_and_machine_learning DML Performance t_full Analytical_and_simulation-based_analysis descartes Prediction Formal_architecture_modeling Meta-models QPN QPME Simulation PRISMA}, month = {March}, note = {Acceptance Rate: 21,9\% (21/96)}, pages = {71–80}, publisher = {IEEE}, title = {{Integrating Statistical Response Time Models in Architectural Performance Models}}, year = 2019 }
%0 Conference Paper %1 EiGrWaKiKo2019-ICSA-Integrating %A Eismann, Simon %A Grohmann, Johannes %A Walter, Jürgen %A von Kistowski, Jóakim %A Kounev, Samuel %B Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) %D 2019 %I IEEE %P 71--80 %R 10.1109/ICSA.2019.00016 %T Integrating Statistical Response Time Models in Architectural Performance Models %U https://doi.org/10.1109/ICSA.2019.00016 %X Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.

Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads. K. Tocze; N. Schmitt; I. Brandic; A. Aral; S. Nadjm-Tehrani; in Proceedings of Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf) as part of FAS*(IEEE ICAC/SASO) conferences companion (2019).
[ BibTeX ] [ EndNote ]
 
@inproceedings{ToScBrArNa2019TowardsEdgeBench, author = {Tocze, Klervie and Schmitt, Norbert and Brandic, Ivona and Aral, Atakan and Nadjm-Tehrani, Simin}, booktitle = {Proceedings of Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf) as part of FAS*(IEEE ICAC/SASO) conferences companion}, keywords = {myown Metrics_and_benchmarking_methodologies Resource_management Performance t_short descartes Cloud Power-energy_efficient_computing Power}, month = {June}, publisher = {IEEE}, title = {{Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads}}, year = 2019 }
%0 Conference Paper %1 ToScBrArNa2019TowardsEdgeBench %A Tocze, Klervie %A Schmitt, Norbert %A Brandic, Ivona %A Aral, Atakan %A Nadjm-Tehrani, Simin %B Proceedings of Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf) as part of FAS*(IEEE ICAC/SASO) conferences companion %D 2019 %I IEEE %T Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads

Artificial Intelligence in Medicine - From Data Collection to Prediction. M. Züfle; R. Leppich; (2019, November).
[ BibTeX ] [ EndNote ]
 
@misc{talk-ZuLe, author = {Z{ü}fle, Marwin and Leppich, Robert}, howpublished = {Invited Talk at Klinikum N{ü}rnberg}, keywords = {Statistical_estimation_and_machine_learning myown Health t_talk descartes Cloud Prediction Talks IoT Sports}, month = {November}, title = {{Artificial Intelligence in Medicine - From Data Collection to Prediction}}, year = 2019 }
%0 Generic %1 talk-ZuLe %A Züfle, Marwin %A Leppich, Robert %D 2019 %T Artificial Intelligence in Medicine - From Data Collection to Prediction

Performance-feedback autoscaling with budget constraints for cloud-based workloads of workflows. A. Ilyushkin; A. Bauer; A. V. Papadopoulos; E. Deelman; A. Iosup; in arXiv preprint arXiv:1905.10270 (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@article{IlBaPaDeIo-Tech-BudgetAS, author = {Ilyushkin, Alexey and Bauer, Andr{é} and Papadopoulos, Alessandro V and Deelman, Ewa and Iosup, Alexandru}, journal = {arXiv preprint arXiv:1905.10270}, keywords = {cloud autoscaling descartes metrics_and_benchmarking_methodologies elasticity t_techreport sys:relevantfor:se-group constraints budget}, title = {Performance-feedback autoscaling with budget constraints for cloud-based workloads of workflows}, year = 2019 }
%0 Journal Article %1 IlBaPaDeIo-Tech-BudgetAS %A Ilyushkin, Alexey %A Bauer, André %A Papadopoulos, Alessandro V %A Deelman, Ewa %A Iosup, Alexandru %D 2019 %J arXiv preprint arXiv:1905.10270 %T Performance-feedback autoscaling with budget constraints for cloud-based workloads of workflows

Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models. S. Trapp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Dezember).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Trapp2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Trapp, Simon}, keywords = {se2_mastersthesis Thesis_supervised_by_Simon_Eismann descartes}, month = {December}, school = {University of W{ü}rzburg}, title = {Exploring the {L}imitations of {S}tatistical {R}esponse {T}ime {M}odels in {A}rchitectural {P}erformance {M}odels}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Trapp2019 %A Trapp, Simon %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models

Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen. L. Hick; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Februar).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Hick2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hick, Lars}, keywords = {Thesis_supervised_by_SE_member se2_mastersthesis Thesis_supervised_by_Simon_Eismann descartes}, month = {February}, school = {University of W{ü}rzburg}, title = {{Erweiterung von Komponenten-basierten Performance Modellen f{ü}r Micro-service Architekturen}}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Hick2019 %A Hick, Lars %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen

Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation. F. Erhard; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, September).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Erhard2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Erhard, Florian}, keywords = {Thesis_supervised_by_SE_member feature_engineering Thesis_supervised_by_Christian_Krupitzer se2_mastersthesis descartes statistical_estimation_and_machine_learning Thesis_supervised_by_Marwin_Zuefle prediction}, month = {September}, school = {University of W{ü}rzburg}, title = {{Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation}}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Erhard2019 %A Erhard, Florian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation

Online model learning for self-aware computing infrastructures. S. Spinner; J. Grohmann; S. Eismann; S. Kounev; in Journal of Systems and Software (2019). 147 1–16.
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Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.
@article{SpGrEiKo2019-JSS-ModelLearning, abstract = {Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.}, author = {Spinner, Simon and Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, journal = {Journal of Systems and Software}, keywords = {DML Performance Analytical_and_simulation-based_analysis descartes Prediction Automated_model_learning Meta-models LibReDE Simulation Cloud Application-aware t_journalmagazine Virtualization}, pages = {1–16}, title = {Online model learning for self-aware computing infrastructures}, volume = 147, year = 2019 }
%0 Journal Article %1 SpGrEiKo2019-JSS-ModelLearning %A Spinner, Simon %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %D 2019 %J Journal of Systems and Software %P 1--16 %R 10.1016/j.jss.2018.09.089 %T Online model learning for self-aware computing infrastructures %U https://doi.org/10.1016/j.jss.2018.09.089 %V 147 %X Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.

An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics. T. Dreher; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Oktober).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Dreher2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Dreher, Tim}, keywords = {Thesis_supervised_by_SE_member feature_engineering Thesis_supervised_by_Christian_Krupitzer se2_mastersthesis descartes Thesis_supervised_by_Marwin_Zuefle thesis_supervised_by_se_member prediction}, month = {October}, school = {University of W{ü}rzburg}, title = {{An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics}}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Dreher2019 %A Dreher, Tim %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics

Autonomic Forecasting Method Selection: Examination and Ways Ahead. M. Züfle; A. Bauer; V. Lesch; C. Krupitzer; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
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@inproceedings{ZuBaLeKrHeKoCu2019-ICAC-Autonomic, author = {Z{ü}fle, Marwin and Bauer, Andr{é} and Lesch, Veronika and Krupitzer, Christian and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 16th {IEEE} International Conference on Autonomic Computing (ICAC)}, keywords = {Statistical_estimation_and_machine_learning Internet_of_Things Performance Online_monitoring_and_forecasting t_full descartes Prediction PRISMA}, month = {June}, publisher = {IEEE}, title = {{Autonomic Forecasting Method Selection: Examination and Ways Ahead}}, year = 2019 }
%0 Conference Paper %1 ZuBaLeKrHeKoCu2019-ICAC-Autonomic %A Züfle, Marwin %A Bauer, André %A Lesch, Veronika %A Krupitzer, Christian %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) %D 2019 %I IEEE %T Autonomic Forecasting Method Selection: Examination and Ways Ahead

Chamulteon: Coordinated Auto-Scaling of Micro-Services. A. Bauer; V. Lesch; L. Versluis; A. Ilyushkin; N. Herbst; S. Kounev; in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
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@inproceedings{BaLeVeIlHeKo2019-ICDCS-Chamulteon, author = {Bauer, Andr{é} and Lesch, Veronika and Versluis, Laurens and Ilyushkin, Alexey and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS)}, keywords = {Metrics_and_benchmarking_methodologies Resource_management Online_monitoring_and_forecasting t_full descartes Self-aware-computing Cloud Elasticity Chameleon SPEC}, month = {July}, title = {Chamulteon: Coordinated Auto-Scaling of Micro-Services}, year = 2019 }
%0 Conference Paper %1 BaLeVeIlHeKo2019-ICDCS-Chamulteon %A Bauer, André %A Lesch, Veronika %A Versluis, Laurens %A Ilyushkin, Alexey %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) %D 2019 %T Chamulteon: Coordinated Auto-Scaling of Micro-Services

Measuring the Energy Efficiency of Transactional Loads on GPGPU. J. von Kistowski; J. Pais; T. Wahl; K.-D. Lange; H. Block; J. Beckett; S. Kounev; in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019).
Best Industry Paper Award
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
General Purpose Graphics Processing Units (GPGPUs) are becoming more and more common in current servers and data centers, which in turn consume a significant amount of electrical power. Measuring and benchmarking this power consumption is important as it helps with optimization and selection of these servers. However, benchmarking and comparing the energy efficiency of GPGPU workloads is challenging as standardized workloads are rare and standardized power and efficiency measurement methods and metrics do not exist. In addition, not all GPGPU systems run at maximum load all the time. Systems that are utilized in transactional, request driven workloads, for example, can run at lower utilization levels. Existing benchmarks for GPGPU systems primarily consider performance and are intended only to run at maximum load. They do not measure performance or energy efficiency at other loads. In turn, server energy-efficiency benchmarks that consider multiple load levels do not address GPGPUs. This paper introduces a measurement methodology for servers with GPGPU accelerators that considers multiple load levels for transactional workloads. The methodology also addresses verifiability of results in order to achieve comparability of different device solutions. We analyze our methodology on three different systems with solutions from two different accelerator vendors. We investigate the efficacy of different methods of load levels scaling and our methodology's reproducibility. We show that the methodology is able to produce consistent and reproducible results with a maximum coefficient of variation of 1.4% regarding power consumption.
@inproceedings{KiPaWaLaBlBeKo2019-ICPE-GPGPUPower, abstract = {{General Purpose Graphics Processing Units (GPGPUs) are becoming more and more common in current servers and data centers, which in turn consume a significant amount of electrical power. Measuring and benchmarking this power consumption is important as it helps with optimization and selection of these servers. However, benchmarking and comparing the energy efficiency of GPGPU workloads is challenging as standardized workloads are rare and standardized power and efficiency measurement methods and metrics do not exist. In addition, not all GPGPU systems run at maximum load all the time. Systems that are utilized in transactional, request driven workloads, for example, can run at lower utilization levels. Existing benchmarks for GPGPU systems primarily consider performance and are intended only to run at maximum load. They do not measure performance or energy efficiency at other loads. In turn, server energy-efficiency benchmarks that consider multiple load levels do not address GPGPUs. This paper introduces a measurement methodology for servers with GPGPU accelerators that considers multiple load levels for transactional workloads. The methodology also addresses verifiability of results in order to achieve comparability of different device solutions. We analyze our methodology on three different systems with solutions from two different accelerator vendors. We investigate the efficacy of different methods of load levels scaling and our methodology's reproducibility. We show that the methodology is able to produce consistent and reproducible results with a maximum coefficient of variation of 1.4\% regarding power consumption.}}, address = {New York, NY, USA}, author = {von Kistowski, J{ó}akim and Pais, Johann and Wahl, Tobias and Lange, Klaus-Dieter and Block, Hansfried and Beckett, John and Kounev, Samuel}, booktitle = {Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering}, keywords = {Metrics_and_benchmarking_methodologies Performance t_full descartes Cloud Award SPEC Power-energy_efficient_computing Power}, note = {{<b>Best Industry Paper Award</b>}}, publisher = {ACM}, series = {ICPE '19}, title = {{Measuring the Energy Efficiency of Transactional Loads on GPGPU}}, year = 2019 }
%0 Conference Paper %1 KiPaWaLaBlBeKo2019-ICPE-GPGPUPower %A von Kistowski, Jóakim %A Pais, Johann %A Wahl, Tobias %A Lange, Klaus-Dieter %A Block, Hansfried %A Beckett, John %A Kounev, Samuel %B Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I ACM %T Measuring the Energy Efficiency of Transactional Loads on GPGPU %X General Purpose Graphics Processing Units (GPGPUs) are becoming more and more common in current servers and data centers, which in turn consume a significant amount of electrical power. Measuring and benchmarking this power consumption is important as it helps with optimization and selection of these servers. However, benchmarking and comparing the energy efficiency of GPGPU workloads is challenging as standardized workloads are rare and standardized power and efficiency measurement methods and metrics do not exist. In addition, not all GPGPU systems run at maximum load all the time. Systems that are utilized in transactional, request driven workloads, for example, can run at lower utilization levels. Existing benchmarks for GPGPU systems primarily consider performance and are intended only to run at maximum load. They do not measure performance or energy efficiency at other loads. In turn, server energy-efficiency benchmarks that consider multiple load levels do not address GPGPUs. This paper introduces a measurement methodology for servers with GPGPU accelerators that considers multiple load levels for transactional workloads. The methodology also addresses verifiability of results in order to achieve comparability of different device solutions. We analyze our methodology on three different systems with solutions from two different accelerator vendors. We investigate the efficacy of different methods of load levels scaling and our methodology's reproducibility. We show that the methodology is able to produce consistent and reproducible results with a maximum coefficient of variation of 1.4% regarding power consumption.

Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; (2019, Juni).
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@misc{talk-Bauer-HotCloudPerf2019, author = {Bauer, Andr{é}}, howpublished = {Talk at HotCloudperf 2019}, keywords = {Metrics_and_benchmarking_methodologies Resource_management Online_monitoring_and_forecasting se2_talk t_talk descartes Cloud Elasticity BUNGEE LibReDE Chameleon}, month = {June}, title = {{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}}, year = 2019 }
%0 Generic %1 talk-Bauer-HotCloudPerf2019 %A Bauer, André %D 2019 %T Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field

On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. M. D’Angelo; S. Gerasimou; S. Ghahremani; J. Grohmann; I. Nunes; E. Pournaras; S. Tomforde; in Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (2019). 13–24.
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Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.
@inproceedings{SEAMS2019-CollectiveLearningSurvey, abstract = {Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.}, author = {{D'Angelo}, M. and {Gerasimou}, S. and {Ghahremani}, S. and {Grohmann}, J. and {Nunes}, I. and {Pournaras}, E. and {Tomforde}, S.}, booktitle = {Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems}, keywords = {Survey Design_of_software_and_systems t_full descartes Self-aware-computing Automated_model_learning Self-adaptive-systems}, month = {may}, pages = {13&ndash;24}, publisher = {IEEE Press}, series = {SEAMS '19}, title = {On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework}, year = 2019 }
%0 Conference Paper %1 SEAMS2019-CollectiveLearningSurvey %A D'Angelo, M. %A Gerasimou, S. %A Ghahremani, S. %A Grohmann, J. %A Nunes, I. %A Pournaras, E. %A Tomforde, S. %B Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems %D 2019 %I IEEE Press %P 13--24 %R 10.1109/SEAMS.2019.00012 %T On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework %U https://doi.org/10.1109/SEAMS.2019.00012 %X Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications. S. Herrnleben; (2019, April).
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@misc{He2019-SelPhyS-Car2Cloud, author = {Herrnleben, Stefan}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2019}, keywords = {Internet_of_Things descartes Application-aware Talks Self-adaptive-systems t_workshop}, month = {April}, title = {{A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications}}, year = 2019 }
%0 Generic %1 He2019-SelPhyS-Car2Cloud %A Herrnleben, Stefan %D 2019 %T A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications

Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. V. Lesch; C. Krupitzer; S. Tomforde; in Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019 (2019).
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