Publications

Publications of Software-Engineering Group / Descartes Research Group

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2021 [ nach oben ]

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 ]

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 ]

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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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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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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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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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.

[ Abstract ] [ BibTeX ] [ EndNote ] [ DOI ] [ Download ]

@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&ndash;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 ]

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%

[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]

Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads. J. Domaschka; S. Eismann; M. Leznik; J. Grohmann; S. Kounev; D. Seybold; (2021).

[ BibTeX ] [ EndNote ]

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).

To appear

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2020 [ nach oben ]

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

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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).

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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 ]

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 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 ]

@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
}

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 ]

@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&ndash;240},
  publisher = {Springer},
  series = {Communications in Computer and Information Science},
  title = {Optimizing Parametric Dependencies for Incremental Performance Model Extraction},
  volume = 1269,
  year = 2020
}

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).

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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 ]

@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
}

Predicting Performance Degradations of Black-box Microservice Applications M. Straesser; (2020).

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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).

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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 ]

@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&ndash;8},
  publisher = {IEEE},
  series = {MASCOTS '20},
  title = {Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS},
  year = 2020
}

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).

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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 IJMERR, (P. R. (Chunhui) Yang, Hrsg.) (2020). 9(12) 1532–1540.

[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]

@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 = {IJMERR},
  keywords = {comparison descartes robotics survey sys:relevantfor:se-group 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 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.

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 ]

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.

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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 ]

@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&ndash;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.

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 ]

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 ]

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).

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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).

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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).

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Toward a Framework for Self-Learning Adaptation Planning through Optimization. V. Lesch; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).

To appear

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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 ]

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)

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@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&ndash;276},
  publisher = {Association for Computing Machinery (ACM)},
  series = {ICPE '20},
  title = {Predicting the Costs of Serverless Workflows},
  year = 2020
}

Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation’’. C. Krupitzer; B. Eberhardinger; I. Gerostathopoulos; C. Raibulet; (2020).

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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).

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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.

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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).

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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).

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@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
}

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.

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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).

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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)

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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 ]

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 ]

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.

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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)

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@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
}

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).

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2019 [ nach oben ]

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).

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Measuring and Rating the Energy-Efficiency of Servers. J. von Kistowski; K.-D. Lange; J. A. Arnold; J. Beckett; H. Block; M. Tricker; S. Sharma; J. Pais; S. Kounev; in Future Generation Computer Systems (2019). 100 579–589.

[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]

@article{KiLaArBeBlTrShPaKo-FGCS2019-ServerEfficiency,
  abstract = {Data centers and servers consume a significant amount of electrical power. As future data centers increase in size, it becomes increasingly important to be able to select servers based on their energy efficiency. Rating the efficiency of servers is challenging, as it depends on the software executed on that server and on its load profile. To account for this, a measurement and rating methodology for servers must make use of both realistic and varying workloads. Existing energy-efficiency benchmarks either run standardized application loads at multiple load levels or multiple micro-benchmarks at full load. This does not enable a full analysis of system behavior as the energy efficiency for different kinds of software at low load levels remains unknown. This article introduces a measurement methodology and metrics for energy-efficiency rating of servers. The methodology defines system setup and instrumentation for reproducible results. It is designed to use multiple, specifically chosen workloads at different load levels for a full system characterization. All partial results are aggregated in a final metric that can be used by regulators to define server-labeling criteria. The methodology and workloads have been implemented in the standardized Server Efficiency Rating Tool. We show the applicability and use of the measurement methodology specifically considering its reproducibility, fairness, and relevance. A reproducibility analysis shows that efficiency scores vary with a maximum coefficient of 1.04\% for repeated experiments. In addition, we evaluate the proposed metrics by investigating their energy-efficiency rating on a set of 385 different servers and show the relevance of the selected workloads by analyzing relationships to real-world applications.},
  author = {von Kistowski, J{ó}akim and Lange, Klaus-Dieter and Arnold, Jeremy A. and Beckett, John and Block, Hansfried and Tricker, Mike and Sharma, Sanjay and Pais, Johann and Kounev, Samuel},
  journal = {Future Generation Computer Systems},
  keywords = {Tool Metrics_and_benchmarking_methodologies Performance descartes t_journalmagazine SPEC Power-energy_efficient_computing Power},
  pages = {579 - 589},
  title = {{Measuring and Rating the Energy-Efficiency of Servers}},
  volume = 100,
  year = 2019
}

%0 Journal Article
%1 KiLaArBeBlTrShPaKo-FGCS2019-ServerEfficiency
%A von Kistowski, Jóakim
%A Lange, Klaus-Dieter
%A Arnold, Jeremy A.
%A Beckett, John
%A Block, Hansfried
%A Tricker, Mike
%A Sharma, Sanjay
%A Pais, Johann
%A Kounev, Samuel
%D 2019
%J Future Generation Computer Systems
%P 579 - 589
%T Measuring and Rating the Energy-Efficiency of Servers
%U http://www.sciencedirect.com/science/article/pii/S0167739X18312123
%V 100
%X Data centers and servers consume a significant amount of electrical power. As future data centers increase in size, it becomes increasingly important to be able to select servers based on their energy efficiency. Rating the efficiency of servers is challenging, as it depends on the software executed on that server and on its load profile. To account for this, a measurement and rating methodology for servers must make use of both realistic and varying workloads. Existing energy-efficiency benchmarks either run standardized application loads at multiple load levels or multiple micro-benchmarks at full load. This does not enable a full analysis of system behavior as the energy efficiency for different kinds of software at low load levels remains unknown. This article introduces a measurement methodology and metrics for energy-efficiency rating of servers. The methodology defines system setup and instrumentation for reproducible results. It is designed to use multiple, specifically chosen workloads at different load levels for a full system characterization. All partial results are aggregated in a final metric that can be used by regulators to define server-labeling criteria. The methodology and workloads have been implemented in the standardized Server Efficiency Rating Tool. We show the applicability and use of the measurement methodology specifically considering its reproducibility, fairness, and relevance. A reproducibility analysis shows that efficiency scores vary with a maximum coefficient of 1.04% for repeated experiments. In addition, we evaluate the proposed metrics by investigating their energy-efficiency rating on a set of 385 different servers and show the relevance of the selected workloads by analyzing relationships to real-world applications.

Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge. S. Kounev; (2019, Juni).

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TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research. S. Eismann; (2019, Februar).

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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).

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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

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@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.

Predicting Server Power Consumption from Standard Rating Results. J. von Kistowski; J. Grohmann; N. Schmitt; S. Kounev; in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019). 301–312.

Full Paper Acceptance Rate: 18.6% (13/70)

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@inproceedings{KiGrScKo2019-ICPE-PowerPrediction,
  abstract = {Data center providers and server operators try to reduce the power consumption of their servers. Finding an energy efficient server for a specific target application is a first step in this regard. Estimating the power consumption of an application on an unavailable server is difficult, as nameplate power values are generally overestimations. Offline power models are able to predict the consumption accurately, but are usually intended for system design, requiring very specific and detailed knowledge about the system under consideration. In this paper, we introduce an offline power prediction method that uses the results of standard power rating tools. The method predicts the power consumption of a specific application for multiple load levels on a target server that is otherwise unavailable for testing. We evaluate our approach by predicting the power consumption of three applications on different physical servers. Our method is able to achieve an average prediction error of 9.49% for three workloads running on real-world, physical servers.},
  address = {New York, NY, USA},
  author = {von Kistowski, J{ó}akim and Grohmann, Johannes and Schmitt, Norbert and Kounev, Samuel},
  booktitle = {Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering},
  keywords = {Statistical_estimation_and_machine_learning Metrics_and_benchmarking_methodologies t_full descartes Prediction PRISMA SPEC Power},
  note = {{Full Paper Acceptance Rate: 18.6\% (13/70)}},
  pages = {301&ndash;312},
  publisher = {Association for Computing Machinery (ACM)},
  series = {ICPE '19},
  title = {{Predicting Server Power Consumption from Standard Rating Results}},
  year = 2019
}

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.

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@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 &mdash; such as load testing &mdash; 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&ndash;50},
  publisher = {Association for Computing Machinery (ACM)},
  series = {ICPE '19},
  title = {{How is Performance Addressed in DevOps?}},
  year = 2019
}

Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. J. C. Walter; (2019).

[ BibTeX ] [ EndNote ] [ URL ]

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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Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report. (SPEC-RG-2019-04), A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. Abad; J. N. Amaral; P. Tuma; A. Iosup; (2019).

[ BibTeX ] [ EndNote ] [ URL ]

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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Measuring, Rating, and Predicting the Energy Efficiency of Servers. J. von Kistowski; (2019, März).

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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 ]

A survey on adaptive authentication. P. Arias-Cabarcos; C. Krupitzer; C. Becker; in ACM Computing Surveys (2019). 52 Art. 80.

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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).

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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).

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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).

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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; N. Herbst; S. Spinner; A. Ali-Eldin; S. Kounev; in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.

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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)

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@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&ndash;80},
  publisher = {IEEE},
  title = {{Integrating Statistical Response Time Models in Architectural Performance Models}},
  year = 2019
}

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.

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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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SPEC Research Group Newsletter, vol. 2 no. 4. S. Kounev; A. van Hoorn; A. Bauer; N. Herbst; (2019, April).

Published by Standard Performance Evaluation Corporation (SPEC)

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Best Practices for Time Series Forecasting. A. Bauer; M. Züfle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019).

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Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud. F. Metzger; T. Hoßfeld; A. Bauer; S. Kounev; P. E. Heegaard; in Proceedings of the IEEE (2019). 107(4) 679–694.

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Performance Oriented Dynamic Bypassing for Intrusion Detection Systems. L. Iffländer; J. Stoll; N. Rawtani; V. Lesch; K.-D. Lange; S. Kounev; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 159–166.

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@inproceedings{Ifflaender2019_PerformanceOrientedDynamicBypassingforIntrusionDetectionSystems,
  abstract = {Attacks on software systems are becoming more and more frequent, aggressive and sophisticated. With the changing threat landscape, in 2018, organizations are looking at when they will be attacked, not if. Intrusion Detection Systems (IDSs) can help in defending against these attacks. The systems that host IDSs require extensive computing resources as IDSs tend to detect attacks under overloaded conditions wrongfully. With the end of Moore's law and the growing adoption of Internet of Things, designers of security systems can no longer expect processing power to keep up the pace with them. This limitation requires ways to increase the performance of these systems without adding additional compute power. In this work, we present two dynamic and a static approach to bypass IDS for traffic deemed benign. We provide its prototype implementation and evaluate our solution. Our evaluation shows promising results. Performance is increased up to the level of a system without an IDS. Attack detection is within the margin of error from the 100% rate. However, our findings show that dynamic approaches perform best when using software switches. The use of a hardware switch reduces the detection rate and performance significantly.},
  address = {New York, NY, USA},
  author = {Iffländer, Lukas and Stoll, Jonathan and Rawtani, Nishant and Lesch, Veronika and Lange, Klaus-Dieter and Kounev, Samuel},
  booktitle = {Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering},
  keywords = {Metrics_and_benchmarking_methodologies Resource_management Networking Security Performance t_short descartes Self-aware-computing EvIDencE Self-adaptive-systems Optimization},
  pages = {159&ndash;166},
  publisher = {ACM},
  series = {ICPE '19},
  title = {Performance Oriented Dynamic Bypassing for Intrusion Detection Systems},
  year = 2019
}

Towards Adaptive Car-to-Cloud Communication. S. Herrnleben; M. Pfannemüller; C. Krupitzer; S. Kounev; M. Segata; F. Fastnacht; M. Nigmann; in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019).

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@inproceedings{HePfKrKoSeFaNi2019-PerAwareCity-carToCloudCommunication,
  abstract = {Recent developments in communication technology have led to cloud resources becoming ubiquitous. These resources enable many new applications by offering computational power for remote embedded devices. In combination with advances in the area of smart driving, this seems to be especially beneficial for applications such as remote maintenance of vehicles or integration with smart city services. As autonomous driving continues to gain traction, Car-to-Cloud communication can support transferring collected data to the cloud, e.g., for dynamic learning of new map information. Additionally, passengers can benefit from novel entertainment services. All these developments require a stable connection between a mobile vehicle and the cloud resources. In this vision paper, we survey Car-to-Cloud communication applications. Based on the analysis of the varying requirements for these applications, we formulate research questions and challenges. Further, we discuss how these challenges can be addressed by means of an adaptive Car-to-Cloud communication middleware. We conclude with an overview on our activities in this area and an outlook on our planned future work on adaptive communication.},
  address = {Kyoto, Japan},
  author = {Herrnleben, Stefan and Pfannem{ü}ller, Martin and Krupitzer, Christian and Kounev, Samuel and Segata, Michele and Fastnacht, Felix and Nigmann, Magnus},
  booktitle = {Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},
  keywords = {Internet_of_Things Cyber-Physical_Systems Networking descartes Self-aware-computing Cloud MODELS Self-adaptive-systems t_workshop Intelligent_Transportation_Systems},
  title = {Towards Adaptive {Car-to-Cloud} Communication},
  year = 2019
}

Beyond position-awareness -- Extending a self-adaptive fall detection system. C. Krupitzer; T. Sztyler; J. Edinger; M. Breitbach; H. Stuckenschmidt; C. Becker; in Pervasive and Mobile Computing (2019). 58

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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).

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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)

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@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&ndash;322},
  publisher = {IEEE Computer Society},
  series = {MASCOTS '19},
  title = {{Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques}},
  year = 2019
}

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.

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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).

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Artificial Intelligence in Medicine - From Data Collection to Prediction. M. Züfle; R. Leppich; (2019, November).

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The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms. E. van Eyk; J. Grohmann; S. Eismann; A. Bauer; L. Versluis; L. Toader; N. Schmitt; N. Herbst; C. L. Abad; A. Iosup; in IEEE Internet Computing (2019). 23(6) 7–18.

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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).

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Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. N. Herbst; (2019, Juni).

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Hands Off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences L. Iffländer; A. Dmitrienko; C. Hagen; M. Jobst; S. Kounev; (2019).

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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

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Discrete-Time Analysis of the Blockchain Distributed Ledger Technology. S. Geissler; T. Prantl; S. Lange; F. Wamser; T. Hossfeld; in Proceedings of the 31st International Teletraffic Congress (ITC) (2019).

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From the Internet-of-Things to the Internet-of-Thinking-Things. S. Kounev; (2019, Juli).

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Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations. E. M. Fredericks; I. Gerostathopoulos; C. Krupitzer; T. Vogel; in Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (2019).

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Towards a QoS-aware Cyber Physical Networking Middleware Architecture. M. Brinkschulte; C. Becker; C. Krupitzer; in 1st International Workshop on Middleware for Lightweight, Spontaneous Environments (MISE) @ ACM/IFIP/USENIX International Middleware Conference (2019).

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A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications. S. Herrnleben; (2019, April).

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Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen. J. Stoll; L. Iffländer; (2019, April).

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WheelShare: Crowd-sensed Surface Classification for Accessible Routing. J. Edinger; V. Raychoudhury; A. Hofmann; A. Wachner; C. Becker; C. Krupitzer; in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019).

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@inproceedings{EdRaHoWaBeKr2019-TSUM-wheelShareConcept,
  abstract = {Accessible path routing for wheeled mobility is an important problem given the permanent and temporary obstacles in the built environment. Existing research works have focused on identifying several obstacles as well as facilities such as crosswalks with traffic signals using smartphone based sensing or crowd-sourcing and used those knowledge to generate accessible routes. While all those research works have identified the nature of the road or sidewalk surface (even/uneven) as a major accessibility concern, there is no in-depth empirical research regarding how the accessibility varies from surface to surface. In this work, we propose a hybrid system, called WheelShare which can clearly classify (in/)accessible surfaces.We have analyzed vibration (using an aide-propelled manual wheelchair) data from 11 different standard indoor and outdoor surfaces found in the built environment and classified them using WheelShare into five different categories across the accessibility spectrum with an accuracy of up to 95\%.Our objective knowledge of surfaces is then applied to generate an accessible route through the best possible surface depending on user and wheelchair requirements. WheelShare is 1) scalable, as it uses crowd-sensing to collect data, 2) dynamic, as the data gets constantly updated, and 3) objective, as it uses an objective and data-centric approach.},
  address = {Kyoto, Japan},
  author = {Edinger, Janick and Raychoudhury, Vaskar and Hofmann, Alexandra and Wachner, Anton and Becker, Christian and Krupitzer, Christian},
  booktitle = {Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},
  keywords = {Internet_of_Things descartes Self-aware-computing Self-adaptive-systems Optimization},
  title = {{WheelShare: Crowd-sensed Surface Classification for Accessible Routing}},
  year = 2019
}

Challenges and Approaches: Forecasting for Autonomic Computing. A. Bauer; in Organic Computing: Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (Hrsg.) (2019).

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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; (2019, Juni).

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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.

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