Publications

2021 [ nach oben ]

• 
  A Comparison of Mechanisms for Compensating Negative Impacts of System Integration. Lesch, Veronika; Krupitzer, Christian; Stubenrauch, Kevin; Keil, Nico; Becker, Christian; Kounev, Samuel; Segata, Michele; in Future Generation Computer Systems (2021). 116 117-131.
  [ BibTeX ] [ EndNote ] [ DOI ] [ Download ]
   
  @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 = {compensation cyber-physical_systems descartes intelligent_transportation_systems internet_of_things platooning system_integration t_journalmagazine}, 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

2020 [ nach oben ]

• 
  Predicting Performance Degradations of Black-box Microservice Applications Straesser, Martin; (2020).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @presentation{straesser2020talk, author = {Straesser, Martin}, keywords = {sys:relevantfor:se-group descartes microservice performance_prediction t_talk}, 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/
• 
  An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things. Krupitzer, Christian; Temizer, Timur; Prantl, Thomas; Raibulet, Claudia; 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 = {sys:relevantfor:se-group cyber-physical_systems descartes internet_of_things self-adaptive-systems t_journalmagazine}, 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.
• 
  Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation''. Krupitzer, Christian; Eberhardinger, Benedikt; Gerostathopoulos, Ilias; Raibulet, Claudia; (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.
• 
  Situation-Aware Meta-Optimization of Platooning Coordination Strategies. Hefter, Johannes; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{hefter2020situationaware, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hefter, Johannes}, keywords = {Analytical_and_simulation-based_analysis Cyber-Physical_Systems Internet_of_Things Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_SE_member Thesis_supervised_by_Veronika_Lesch Tool descartes ntelligent_Transportation_Systems optimization se2_mastersthesis t_studentthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {Situation-Aware Meta-Optimization of Platooning Coordination Strategies}, year = 2020 }
  %0 Thesis %1 hefter2020situationaware %A Hefter, Johannes %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Situation-Aware Meta-Optimization of Platooning Coordination Strategies
• 
  A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation. Züfle, Marwin; Kounev, Samuel; 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 = {descartes online_monitoring_and_forecasting prediction statistical_estimation_and_machine_learning t_short}, 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
• 
  Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms. Müller, Patrick; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{muller2020optimizing, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Müller, Patrick}, keywords = {Industry_4_0 Optimization Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_Veronika_Lesch cyber-physical_systems descartes internet_of_things se2_mastersthesis t_studentthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms}, year = 2020 }
  %0 Thesis %1 muller2020optimizing %A Müller, Patrick %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms
• 
  Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms. Schleicher, Johannes; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{schleicher2020minimizing, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Schleicher, Johannes}, keywords = {Industry_4_0 Optimization Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_Veronika_Lesch cyber-physical_systems descartes internet_of_things se2_mastersthesis t_studentthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms}, year = 2020 }
  %0 Thesis %1 schleicher2020minimizing %A Schleicher, Johannes %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms
• 
  Optimal Platooning Coordination using Multiple Objectives. Hoang Tran, Ha Giang; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{hoangtran2020optimal, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hoang Tran, Ha Giang}, keywords = {Analytical_and_simulation-based_analysis Cyber-Physical_Systems Intelligent_Transportation_Systems Internet_of_Things Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_SE_member Thesis_supervised_by_Veronika_Lesch Tool descartes se2_mastersthesis t_studentthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {Optimal Platooning Coordination using Multiple Objectives}, year = 2020 }
  %0 Thesis %1 hoangtran2020optimal %A Hoang Tran, Ha Giang %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Optimal Platooning Coordination using Multiple Objectives
• 
  Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning. D'Angelo, Mirko; Ghahremani, Sona; Gerasimou, Simos; Grohmann, Johannes; Nunes, Ingrid; Tomforde, Sven; Pournaras, Evangelos; in Proceedings of the 5th eCAS Workshop on Engineering Collective Adaptive Systems in Conjunction with the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020) (2020).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @conference{DaGhGeGrNuToPo2020-eCas-LearningInCAS, author = {D'Angelo, Mirko and Ghahremani, Sona and Gerasimou, Simos and Grohmann, Johannes and Nunes, Ingrid and Tomforde, Sven and Pournaras, Evangelos}, booktitle = {Proceedings of the 5th eCAS Workshop on Engineering Collective Adaptive Systems in Conjunction with the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020)}, keywords = {Statistical_estimation_and_machine_learning descartes myown t_workshop}, 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 Proceedings of the 5th eCAS Workshop on Engineering Collective Adaptive Systems in Conjunction with the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020) %D 2020 %T Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning
• 
  REACT: A Model-Based Runtime Environment for Adapting Communication Systems. Pfannemüller, Martin; Breitbach, Martin; Krupitzer, Christian; Weckesser, Markus; Becker, Christian; Schmerl, Bradley; Schürr, Andy; 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 = {sys:relevantfor:se-group cyber-physical_systems descartes internet_of_things self-adaptive-systems t_full}, 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.
• 
  Predicting Performance Degradations of Microservice Applications. Straesser, Martin; Thesis; University of Wuerzburg. (2020).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @mastersthesis{straesser2020predicting, author = {Straesser, Martin}, keywords = {sys:relevantfor:se-group descartes microservice performance_prediction t_studentthesis}, month = {may}, school = {University of Wuerzburg}, title = {Predicting Performance Degradations of Microservice Applications}, year = 2020 }
  %0 Thesis %1 straesser2020predicting %A Straesser, Martin %D 2020 %T Predicting Performance Degradations of Microservice Applications
• 
  A Review of Serverless Use Cases and their Characteristics Eismann, Simon; Scheuner, Joel; van Eyk, Erwin; Schwinger, Maximilian; Grohmann, Johannes; Herbst, Nikolas; Abad, Cristina; Iosup, Alexandru; (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 = {descartes myown 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.
• 
  Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario. Prantl, Thomas; Ten, Peter; Iffländer, Lukas; Dmitrenko, Alexandra; Kounev, Samuel; Krupitzer, Christian; 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 = {Group_Encryption Internet_of_Things t_full Security myown Performance_Evaluation descartes}, 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.
• 
  Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study. Düking, Peter; Holmberg, Hans-Christer; Kunz, Philipp; Leppich, Robert; Sperlich, Billy; 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 = {descartes myown sport t_full 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
• 
  Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS. Grohmann, Johannes; Seybold, Daniel; Eismann, Simon; Leznik, Mark; Kounev, Samuel; Domaschka, Jörg; in 2020 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020).

  Acceptance Rate: 27%
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {automated_model_learning descartes myown performance performance_modeling prediction statistical_estimation_and_machine_learning t_full}, month = {November}, note = {Acceptance Rate: 27%}, 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 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) %D 2020 %T Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS %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.
• 
  Optimizing Parametric Dependencies for Incremental Performance Model Extraction. Voneva, Sonya; Mazkatli, Manar; Grohmann, Johannes; Koziolek, Anne; in Companion of the 14th European Conference Software Architecture (ECSA 2020), H. Muccini, Avgeriou, P., Buhnova, B., Cámara, J., Caporuscio, M., Franzago, M., Koziolek, A., Scandurra, P., Trubiani, C., Weyns, D., Zdun, U. (eds.) (2020). (Vol. 1269) 228--240.
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @inproceedings{VoMaGrKo-ECSA-Dependencies, 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 = {descartes myown parametric_dependencies performance prediction statistical_estimation_and_machine_learning 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) %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 %T Optimizing Parametric Dependencies for Incremental Performance Model Extraction %U https://doi.org/10.1007/978-3-030-59155-7_17 %V 1269
• 
  Quantifying measurement quality and load distribution in Tor. Greubel, Andre; Pohl, Steffen; Kounev, Samuel; 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 = {TLBM t_full tor balancing descartes}, 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.
• 
  Serverless Applications:Why, When, and How? Eismann, Simon; Joel, Scheuner; van Eyk, Erwin; Schwinger, Maximilian; Grohmann, Johannes; Herbst, Nikolas; Abad, Cristina; Iosup, Alexandru; in IEEE Software (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  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 = {descartes myown t_journalmagazine}, title = {Serverless Applications:Why, When, and How?}, year = 2020 }
  %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 2020 %J IEEE Software %T Serverless Applications:Why, When, and How? %U https://ieeexplore.ieee.org/document/9190031 %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.
• 
  SIMPL: Secure IoT Management Platform. Prantl, Thomas; Ben Yahya, Ala Eddine; Dmitrienko, Alexandra; Kounev, Samuel; Lipp, Fabian; Hock, David; Rathfelder, Christoph; Hofherr, Martin; 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 = {t_workshop myown internet_of_things SIMPL descartes}, 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. Zinner, Christoph; Matzka, Manuel; Leppich, Robert; Kounev, Samuel; Holmberg, HC; Sperlich, Billy; 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 = {covid19 descartes health myown t_full t_journalmagazine}, 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 Simulation-based Optimization Framework for Online Adaptation of Networks. Herrnleben, Stefan; Grohmann, Johannes; Rygielski, Pitor; Lesch, Veronika; Krupitzer, Christian; Kounev, Samuel; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  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.}, 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)}, keywords = {descartes myown networking t_full}, month = {August}, series = {SIMUtools 2020}, title = {A Simulation-based Optimization Framework for Online Adaptation of Networks}, year = 2020 }
  %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) %D 2020 %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.
• 
  Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms. König, Maximilian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{konig2020solving, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {König, Maximilian}, keywords = {Cyber-Physical_Systems Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_Veronika_Lesch descartes intelligent_transportation_systems optimization se2_mastersthesis}, month = {January}, school = {University of W{ü}rzburg}, title = {Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms}, type = {{Master Thesis}}, year = 2020 }
  %0 Thesis %1 konig2020solving %A König, Maximilian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms
• 
  A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. Grohmann, Johannes; Herbst, Nikolas; Chalbani, Avi; Arian, Yair; Peretz, Noam; Kounev, Samuel; in Computers (2020). 9(1) 10.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]
   
  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 = {automated_model_learning descartes performance se2 t_journalmagazine}, 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 %T A Taxonomy of Techniques for SLO Failure Prediction in Software Systems %U https://www.mdpi.com/2073-431X/9/1/10 %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.
• 
  Time Series Forecasting for Self-Aware Systems. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Zehe, Albin; Hotho, Andreas; Kounev, Samuel; 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 = {Elasticity Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting Prediction Self-aware-computing Telescope descartes 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
• 
  Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation. Hartmann, Stephan; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{hartmann2020minimising, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hartmann, Stephan}, keywords = {Industry_4_0 Optimization Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_Veronika_Lesch cyber-physical_systems descartes internet_of_things se2_mastersthesis}, month = {March}, school = {University of W{ü}rzburg}, title = {Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation}, type = {{Master Thesis}}, year = 2020 }
  %0 Thesis %1 hartmann2020minimising %A Hartmann, Stephan %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation
• 
  Towards a Self-Aware Prediction of Critical States. Züfle, Marwin; in Organic Computing: Doctoral Dissertation Colloquium 2020, S. Tomforde, Krupitzer, C. (eds.) (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 = {Self-aware-computing descartes online_monitoring_and_forecasting prediction statistical_estimation_and_machine_learning 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
• 
  SPEC Research Group Newsletter, vol. 3 no. 1. Kounev, Samuel; van Hoorn, Andre; Bauer, André; Herbst, Nikolas; (2020).

  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 = {descartes se2 t_other}, 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
• 
  Toward a Framework for Self-Learning Adaptation Planning through Optimization. Lesch, Veronika; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).

  To appear
  [ BibTeX ] [ EndNote ]
   
  @incollection{lesch2020toward, author = {Lesch, Veronika}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, keywords = {intelligent_transportation_systems t_full myown optimization cyber-physical_systems descartes self-aware-computing industry_4_0}, note = {To appear}, 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
• 
  Microservices: A Performance Tester's Dream or Nightmare? Eismann, Simon; Bezemer, Cor-Paul; Shang, Weiyi; Okanovic, Dusan; van Hoorn, Andre; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020).

  Acceptance Rate: 23.4% (15/64)
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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? %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.
• 
  Beyond Microbenchmarks: The SPEC-RG Vision for A Comprehensive Serverless Benchmark. van Eyk, Erwin; Scheuner, Joel; Eismann, Simon; Abad, Cristina; Iosup, Alexandru; 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 = {Metrics_and_benchmarking_methodologies Performance SPEC descartes t_visionposition}, 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 Failures by Means of Machine Learning Methods on the Example of an Industrial Press. Agne, Joachim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Agne2020, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Agne, Joachim}, keywords = {Industry_4_0 Thesis_supervised_by_Marwin_Zuefle Online_monitoring_and_forecasting descartes feature_engineering Thesis_supervised_by_SE_member Cyber-Physical_Systems se2_mastersthesis}, 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
• 
  Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. Papadopoulos, Alessandro Vittorio; Versluis, Laurens; Bauer, André; Herbst, Nikolas; von Kistowski, Jóakim; Ali-Eldin, Ahmed; Abad, Cristina L.; Amaral, José Nelson; Tuma, Petr; Iosup, Alexandru; in Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria, M. Felderer, Hasselbring, W., Rabiser, R., Jung, R. (eds.) (2020). (Vol. 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 = {descartes se2 t_extendedabstract}, 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
• 
  Energy Efficiency Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite. Schmitt, Norbert; Bucek, James; Lange, Klaus-Dieter; Kounev, Samuel; 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 = {Performance Power Power-energy_efficient_computing SPEC descartes myown t_wip}, 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
• 
  Towards Self-Aware Multirotor Formations. Kaiser, Dennis; Lesch, Veronika; Rothe, Julian; Strohmeier, Michael; Spiess, Florian; Krupitzer, Christian; Montenegro, Sergio; Kounev, Samuel; 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 = {Analytical_and_simulation-based_analysis Cyber-Physical_Systems Design_of_software_and_systems Intelligent_Transportation_Systems Internet_of_Things Self-aware-computing Simulation descartes t_journalmagazine}, 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
• 
  An IoT Network Emulator for Analyzing the Influence of Varying Network Quality. Herrnleben, Stefan; Ailabouni, Rudy; Grohmann, Johannes; Prantl, Thomas; Krupitzer, Christian; Kounev, Samuel; 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 = {descartes internet_of_things myown networking t_full}, 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.
• 
  A Survey on Predictive Maintenance for Industry 4.0 Krupitzer, Christian; Wagenhals, Tim; Züfle, Marwin; Lesch, Veronika; Schäfer, Dominik; Mozaffarin, Amin; Edinger, Janick; Becker, Christian; Kounev, Samuel; (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 = {Cyber-Physical_Systems Industry_4_0 Internet_of_Things Online_monitoring_and_forecasting Prediction Reliability Survey descartes t_techreport}, 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
• 
  An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. Bauer, André; Züfle, Marwin; Grohmann, Johannes; Schmitt, Norbert; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) (2020).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @inproceedings{BaZuGrScHeKo-ICPE20-Seasonal-Forecast, 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 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020)}, keywords = {Online_monitoring_and_forecasting PRISMA Prediction descartes t_short}, month = {April}, publisher = {ACM}, 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 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) %C New York, NY, USA %D 2020 %I ACM %T An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series %U https://doi.org/10.1145/3358960.3379123
• 
  A Concept for Crowd-sensed Prediction of Mobile Network Connectivity. Herrnleben, Stefan; Zeidler, Bernd; Züfle, Marwin; Krupitzer, Christian; Kounev, Samuel; 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 = {Application-aware Automated_model_learning Cloud Internet_of_Things Networking Prediction Self-adaptive-systems descartes 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
• 
  Predicting the Costs of Serverless Workflows. Eismann, Simon; Grohmann, Johannes; van Eyk, Erwin; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020).

  Acceptance Rate: 23.4% (15/64)
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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%.}, 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 = {descartes se2 t_full}, month = {April}, note = {{Acceptance Rate: 23.4% (15/64)}}, 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) %D 2020 %T Predicting the Costs of Serverless Workflows %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%.
• 
  Model-based Performance Predictions for SDN-based Networks: A Case Study. Herrnleben, Stefan; Rygielski, Pitor; Grohmann, Johannes; Eismann, Simon; Hossfeld, Tobias; Kounev, Samuel; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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.}, author = {Herrnleben, Stefan and Rygielski, Pitor 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 = {descartes SDN t_full}, month = {March}, 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, Pitor %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 %D 2020 %T Model-based Performance Predictions for SDN-based Networks: A Case Study %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.
• 
  To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows. Züfle, Marwin; Krupitzer, Christian; Erhard, Florian; Grohmann, Johannes; Kounev, Samuel; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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.}, 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 = {Cyber-Physical_Systems Internet_of_Things PRISMA Performance Prediction Reliability Statistical_estimation_and_machine_learning descartes t_full}, month = {March}, 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 %D 2020 %T To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows %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.
• 
  Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; 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 = {Online_monitoring_and_forecasting Prediction Telescope Tool descartes t_short}, 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
• 
  A Survey on Human Machine Interaction in Industry 4.0 Krupitzer, Christian; Müller, Sebastian; Lesch, Veronika; Züfle, Marwin; Edinger, Janick; Lemken, Alexander; Schäfer, Dominik; Kounev, Samuel; Becker, Christian; (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 = {Industry_4_0 Survey descartes t_techreport}, 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
• 
  Incremental Calibration of Architectural Performance Models with Parametric Dependencies. Mazkatli, Manar; Monschein, David; Grohmann, Johannes; Koziolek, Anne; in 2020 IEEE International Conference on Software Architecture (ICSA 2020) (2020). 23--34.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ 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 = {Automated_model_learning descartes modeling myown performance prediction statistical_estimation_and_machine_learning t_full}, 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 %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.

2019 [ nach oben ]

• 
  Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations. Fredericks, Erik M.; Gerostathopoulos, Ilias; Krupitzer, Christian; Vogel, Thomas; in Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{FrGeKrVo2019, author = {Fredericks, Erik M. and Gerostathopoulos, Ilias and Krupitzer, Christian and Vogel, Thomas}, booktitle = {Proceedings of the 13th {IEEE} International Conference on Self-Adaptive and Self-Organizing Systems, {SASO} 2019}, keywords = {Intelligent_Transportation_Systems descartes Internet_of_Things Self-adaptive-systems Cyber-Physical_Systems Self-aware-computing Multi-criteria_optimization Optimization}, month = {June}, publisher = {IEEE}, title = {Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations}, year = 2019 }
  %0 Conference Paper %1 FrGeKrVo2019 %A Fredericks, Erik M. %A Gerostathopoulos, Ilias %A Krupitzer, Christian %A Vogel, Thomas %B Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 %D 2019 %I IEEE %T Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
• 
  Predicting Server Power Consumption from Standard Rating Results. von Kistowski, Jóakim; Grohmann, Johannes; Schmitt, Norbert; Kounev, Samuel; in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019).

  Full Paper Acceptance Rate: 18.6% (13/70)
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  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.
  @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 = {Metrics_and_benchmarking_methodologies PRISMA Power Prediction SPEC Statistical_estimation_and_machine_learning descartes t_full}, note = {{Full Paper Acceptance Rate: 18.6\% (13/70)}}, publisher = {ACM}, series = {ICPE '19}, title = {{Predicting Server Power Consumption from Standard Rating Results}}, year = 2019 }
  %0 Conference Paper %1 KiGrScKo2019-ICPE-PowerPrediction %A von Kistowski, Jóakim %A Grohmann, Johannes %A Schmitt, Norbert %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 Predicting Server Power Consumption from Standard Rating Results %X 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.
• 
  Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud. Metzger, Florian; Hoßfeld, Tobias; Bauer, André; Kounev, Samuel; Heegaard, Poul. E.; in Proceedings of the IEEE (2019). 107(4) 679-694.
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @article{MeHoBaKoHe2019-PIEEE-IoTTraffic, author = {Metzger, Florian and Hoßfeld, Tobias and Bauer, Andr{é} and Kounev, Samuel and Heegaard, Poul. E.}, journal = {Proceedings of the IEEE}, keywords = {Analytical_and_simulation-based_analysis Elasticity Internet_of_Things Performance Resource_management descartes t_journalmagazine}, month = {April}, number = 4, pages = {679-694}, title = {{Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud}}, volume = 107, year = 2019 }
  %0 Journal Article %1 MeHoBaKoHe2019-PIEEE-IoTTraffic %A Metzger, Florian %A Hoßfeld, Tobias %A Bauer, André %A Kounev, Samuel %A Heegaard, Poul. E. %D 2019 %J Proceedings of the IEEE %N 4 %P 679-694 %T Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud %V 107
• 
  Integrating Statistical Response Time Models in Architectural Performance Models. Eismann, Simon; Grohmann, Johannes; Walter, Jürgen; von Kistowski, Jóakim; Kounev, Samuel; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71-80.

  Acceptance Rate: 21,9% (21/96)
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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 = {Analytical_and_simulation-based_analysis DML Formal_architecture_modeling Meta-models PRISMA Performance Prediction QPME QPN Simulation Statistical_estimation_and_machine_learning descartes t_full}, month = {March}, note = {Acceptance Rate: 21,9\% (21/96)}, pages = {71-80}, 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 %P 71-80 %T Integrating Statistical Response Time Models in Architectural Performance Models %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.
• 
  Performance Oriented Dynamic Bypassing for Intrusion Detection Systems. Iffländer, Lukas; Stoll, Jonathan; Rawtani, Nishant; Lesch, Veronika; Lange, Klaus-Dieter; Kounev, Samuel; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 159--166.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  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.
  @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 = {EvIDencE Metrics_and_benchmarking_methodologies Resource_management Self-adaptive-systems Optimization Security Performance t_short descartes Self-aware-computing Networking}, pages = {159–166}, publisher = {ACM}, series = {ICPE '19}, title = {Performance Oriented Dynamic Bypassing for Intrusion Detection Systems}, year = 2019 }
  %0 Conference Paper %1 Ifflaender2019_PerformanceOrientedDynamicBypassingforIntrusionDetectionSystems %A Iffländer, Lukas %A Stoll, Jonathan %A Rawtani, Nishant %A Lesch, Veronika %A Lange, Klaus-Dieter %A Kounev, Samuel %B Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I ACM %P 159--166 %T Performance Oriented Dynamic Bypassing for Intrusion Detection Systems %U http://doi.acm.org/10.1145/3297663.3310313 %X 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.
• 
  Towards Adaptive Car-to-Cloud Communication. Herrnleben, Stefan; Pfannemüller, Martin; Krupitzer, Christian; Kounev, Samuel; Segata, Michele; Fastnacht, Felix; Nigmann, Magnus; in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  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.
  @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 = {Cloud Cyber-Physical_Systems Intelligent_Transportation_Systems Internet_of_Things MODELS Networking Self-adaptive-systems Self-aware-computing descartes t_workshop}, title = {Towards Adaptive {Car-to-Cloud} Communication}, year = 2019 }
  %0 Conference Paper %1 HePfKrKoSeFaNi2019-PerAwareCity-carToCloudCommunication %A Herrnleben, Stefan %A Pfannemüller, Martin %A Krupitzer, Christian %A Kounev, Samuel %A Segata, Michele %A Fastnacht, Felix %A Nigmann, Magnus %B Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) %C Kyoto, Japan %D 2019 %T Towards Adaptive Car-to-Cloud Communication %X 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.
• 
  Performance Influence of Security Function Chain Ordering. Iffländer, Lukas; Fella, Nicolas; 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 = {Application-aware EvIDencE Metrics_and_benchmarking_methodologies Networking Online_monitoring_and_forecasting Optimization Performance Reliability Resource_management SDN Security Self-adaptive-systems Self-aware-computing descartes t_poster}, 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.
• 
  Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen. Stoll, Jonathan; Iffländer, Lukas; (2019).
  [ BibTeX ] [ EndNote ]
   
  @misc{talk-StIf-SDN-buildingIoT, author = {Stoll, Jonathan and Iffländer, Lukas}, howpublished = {building IoT 2019}, keywords = {descartes myown se2_talk t_talk}, 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
• 
  Challenges and Approaches: Forecasting for Autonomic Computing. Bauer, André; in Organic Computing: Doctoral Dissertation Colloquium 2018, S. Tomforde, Sick, B. (eds.) (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @incollection{Ba2019-OCDDC-Forecatsing, author = {Bauer, Andr{é}}, booktitle = {{Organic Computing: Doctoral Dissertation Colloquium 2018}}, editor = {Tomforde, Sven and Sick, Bernhard}, keywords = {Online_monitoring_and_forecasting Self-adaptive-systems Self-aware-computing Telescope descartes t_bookchapter}, month = {April}, publisher = {kassel university press GmbH}, title = {{Challenges and Approaches: Forecasting for Autonomic Computing}}, year = 2019 }
  %0 Book Section %1 Ba2019-OCDDC-Forecatsing %A Bauer, André %B Organic Computing: Doctoral Dissertation Colloquium 2018 %D 2019 %E Tomforde, Sven %E Sick, Bernhard %I kassel university press GmbH %T Challenges and Approaches: Forecasting for Autonomic Computing
• 
  Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. Bauer, André; (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @misc{talk-Bauer-HotCloudPerf2019, author = {Bauer, Andr{é}}, howpublished = {Talk at HotCloudperf 2019}, keywords = {BUNGEE Chameleon Cloud Elasticity LibReDE Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting Resource_management descartes se2_talk t_talk}, 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
• 
  Online model learning for self-aware computing infrastructures. Spinner, Simon; Grohmann, Johannes; Eismann, Simon; Kounev, Samuel; in Journal of Systems and Software (2019). 147 1 - 16.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  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 = {Analytical_and_simulation-based_analysis Application-aware Automated_model_learning Cloud DML LibReDE Meta-models Performance Prediction Simulation Virtualization descartes t_journalmagazine}, 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 %T Online model learning for self-aware computing infrastructures %U http://www.sciencedirect.com/science/article/pii/S0164121218302188 %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%.
• 
  Autonomic Forecasting Method Selection: Examination and Ways Ahead. Züfle, Marwin; Bauer, André; Lesch, Veronika; Krupitzer, Christian; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @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 = {Internet_of_Things Online_monitoring_and_forecasting PRISMA Performance Prediction Statistical_estimation_and_machine_learning descartes t_full}, 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
• 
  Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report. (SPEC-RG-2019-04), Papadopoulos, Alessandro Vittorio; Versluis, Laurens; Bauer, André; Herbst, Nikolas; von Kistowski, Jóakim; Ali-Eldin, Ahmed; Abad, Cristina; Amaral, J. Nelson; Tuma, Petr; Iosup, Alexandru; (2019).
  [ BibTeX ] [ EndNote ] [ URL ]
   
  @techreport{PaVeBaHeKiEAAbAmTuIo2019-SPEC-CloudPerformance, 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}, institution = {SPEC Research Group — Cloud Working Group, Standard Performance Evaluation Corporation (SPEC)}, keywords = {Cloud Metrics_and_benchmarking_methodologies Performance SPEC descartes t_techreport}, month = {April}, number = {SPEC-RG-2019-04}, title = {{Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report}}, year = 2019 }
  %0 Report %1 PaVeBaHeKiEAAbAmTuIo2019-SPEC-CloudPerformance %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 %N SPEC-RG-2019-04 %T Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report %U https://research.spec.org/fileadmin/user_upload/documents/rg_cloud/endorsed_publications/SPEC_RG_2019_Methodological_Principles_for_Reproducible_Performance_Evaluation_in_Cloud_Computing.pdf
• 
  Neukonzeptualisierung des Programmiervorkurses mit Fokus auf Übung und Vertiefung. Migendt, Christian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Migendt2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Migendt, Christian}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Veronika_Lesch descartes se2_mastersthesis}, month = {July}, school = {University of W{ü}rzburg}, title = {{Neukonzeptualisierung des Programmiervorkurses mit Fokus auf {Ü}bung und Vertiefung}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Migendt2019 %A Migendt, Christian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Neukonzeptualisierung des Programmiervorkurses mit Fokus auf Übung und Vertiefung
• 
  Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. Papadopoulos, Alessandro Vittorio; Versluis, Laurens; Bauer, André; Herbst, Nikolas; von Kistowski, Jóakim; Ali-Eldin, Ahmed; Abad, Cristina; Amaral, J. Nelson; Tuma, Petr; Iosup, Alexandru; in IEEE Transactions on Software Engineering (2019). 1-1.
  [ BibTeX ] [ EndNote ]
   
  @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 = {Cloud Metrics_and_benchmarking_methodologies Performance SPEC descartes t_journalmagazine}, month = {July}, pages = {1-1}, title = {Methodological Principles for Reproducible Performance Evaluation in Cloud Computing}, 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 %P 1-1 %T Methodological Principles for Reproducible Performance Evaluation in Cloud Computing
• 
  Measuring, Rating, and Predicting the Energy Efficiency of Servers. von Kistowski, Jóakim; (2019).
  [ BibTeX ] [ EndNote ] [ URL ]
   
  @phdthesis{Kistowski2019-phd, author = {von Kistowski, J{ó}akim}, keywords = {Power Prediction descartes LIMBO Supervised_PhD_Thesis}, month = {March}, school = {University of W{ü}rzburg, Germany}, title = {Measuring, Rating, and Predicting the Energy Efficiency of Servers}, year = 2019 }
  %0 Thesis %1 Kistowski2019-phd %A von Kistowski, Jóakim %D 2019 %T Measuring, Rating, and Predicting the Energy Efficiency of Servers %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/17847
• 
  Measuring the Energy Efficiency of Transactional Loads on GPGPU. von Kistowski, Jóakim; Pais, Johann; Wahl, Tobias; Lange, Klaus-Dieter; Block, Hansfried; Beckett, John; Kounev, Samuel; 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 = {Award Cloud Metrics_and_benchmarking_methodologies Performance Power Power-energy_efficient_computing SPEC descartes t_full}, 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.
• 
  The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms. van Eyk, Erwin; Grohmann, Johannes; Eismann, Simon; Bauer, André; Versluis, Laurens; Toader, Lucian; Schmitt, Norbert; Herbst, Nikolas; Abad, Cristina L.; Iosup, Alexandru; in IEEE Internet Computing (2019). 23(6) 7-18.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, Function-as-a-Service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for systems-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.
  @article{vEGrEiBaVeToScHeAbIo-IC-FaaS, abstract = {Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, Function-as-a-Service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for systems-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.}, author = {van Eyk, Erwin and Grohmann, Johannes and Eismann, Simon and Bauer, Andr{é} and Versluis, Laurens and Toader, Lucian and Schmitt, Norbert and Herbst, Nikolas and Abad, Cristina L. and Iosup, Alexandru}, journal = {IEEE Internet Computing}, keywords = {Cloud Design_of_software_and_systems SPEC Survey descartes t_journalmagazine}, month = {nov}, number = 6, pages = {7-18}, publisher = {IEEE}, title = {The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms}, volume = 23, year = 2019 }
  %0 Journal Article %1 vEGrEiBaVeToScHeAbIo-IC-FaaS %A van Eyk, Erwin %A Grohmann, Johannes %A Eismann, Simon %A Bauer, André %A Versluis, Laurens %A Toader, Lucian %A Schmitt, Norbert %A Herbst, Nikolas %A Abad, Cristina L. %A Iosup, Alexandru %D 2019 %I IEEE %J IEEE Internet Computing %N 6 %P 7-18 %T The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms %U https://ieeexplore.ieee.org/document/8894540 %V 23 %X Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, Function-as-a-Service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for systems-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.
• 
  Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. Walter, Jürgen Christian; (2019).
  [ BibTeX ] [ EndNote ] [ URL ]
   
  @phdthesis{Walter2018-phd, author = {Walter, J{ü}rgen Christian}, keywords = {DML PMX Supervised_PhD_Thesis descartes t_dissertation}, school = {Universit{ä}t W{ü}rzburg}, title = {Automation in Software Performance Engineering Based on a Declarative Specification of Concerns}, type = {doctoralthesis}, year = 2019 }
  %0 Thesis %1 Walter2018-phd %A Walter, Jürgen Christian %D 2019 %T Automation in Software Performance Engineering Based on a Declarative Specification of Concerns %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/18090
• 
  How is Performance Addressed in DevOps? Bezemer, Cor-Paul; Eismann, Simon; Ferme, Vincenzo; Grohmann, Johannes; Heinrich, Robert; Jamshidi, Pooyan; Shang, Weiyi; van Hoorn, André; Villavicencio, Mónica; Walter, Jürgen; Willnecker, Felix; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45--50.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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 &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.}, 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 = {Design_of_software_and_systems Instrumentation_profiling_and_workload_characterization Performance SPEC Survey descartes t_short}, pages = {45&ndash;50}, 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 %D 2019 %P 45--50 %T How is Performance Addressed in DevOps? %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.
• 
  Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen. Hick, Lars; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Hick2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hick, Lars}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis}, 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
• 
  An Evaluation Tool for Cost-Aware Vehicle Route Calculation. Hadry, Marius; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Hadry2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hadry, Marius}, keywords = {Thesis_supervised_by_Veronika_Lesch Intelligent_Transportation_Systems Self-adaptive-systems Thesis_supervised_by_Christian_Krupitzer Optimization se2_mastersthesis Simulation Internet_of_Things Thesis_supervised_by_SE_member descartes Tool Analytical_and_simulation-based_analysis}, month = {August}, school = {University of W{ü}rzburg}, title = {{An Evaluation Tool for Cost-Aware Vehicle Route Calculation}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Hadry2019 %A Hadry, Marius %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T An Evaluation Tool for Cost-Aware Vehicle Route Calculation
• 
  Comparison of Compensation Mechanisms for Intra-Platoon Position Effects. Stubenrauch, Kevin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).

  Würzburg Software Engineering Award sponsored by Bosch Rexroth
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Stubenrauch2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Stubenrauch, Kevin}, keywords = {Thesis_supervised_by_Veronika_Lesch Thesis_supervised_by_Christian_Krupitzer se2_mastersthesis Simulation Internet_of_Things Thesis_supervised_by_SE_member Cyber-Physical_Systems Intelligent_Transportation_Systems descartes Analytical_and_simulation-based_analysis}, month = {July}, note = {<b>W{ü}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{ü}rzburg}, title = {{Comparison of Compensation Mechanisms for Intra-Platoon Position Effects}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Stubenrauch2019 %A Stubenrauch, Kevin %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Comparison of Compensation Mechanisms for Intra-Platoon Position Effects
• 
  Measuring and Rating the Energy-Efficiency of Servers. von Kistowski, Jóakim; Lange, Klaus-Dieter; Arnold, Jeremy A.; Beckett, John; Block, Hansfried; Tricker, Mike; Sharma, Sanjay; Pais, Johann; Kounev, Samuel; in Future Generation Computer Systems (2019). 100 579 - 589.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]
   
  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.
  @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 = {Metrics_and_benchmarking_methodologies Performance Power Power-energy_efficient_computing SPEC Tool descartes t_journalmagazine}, 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.
• 
  Analysis of Centralized Platooning Coordination Approaches. Dalhof, Florian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Dalhof2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Dalhof, Florian}, keywords = {Thesis_supervised_by_Veronika_Lesch Intelligent_Transportation_Systems Thesis_supervised_by_Christian_Krupitzer se2_mastersthesis descartes Internet_of_Things Thesis_supervised_by_SE_member Cyber-Physical_Systems Tool Analytical_and_simulation-based_analysis}, month = {August}, school = {University of W{ü}rzburg}, title = {{Analysis of Centralized Platooning Coordination Approaches}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Dalhof2019 %A Dalhof, Florian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Analysis of Centralized Platooning Coordination Approaches
• 
  Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context. Geiger, Ariane; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Geiger2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Geiger, Ariane}, keywords = {Industry_4_0 Internet_of_Things Networking Reliability Security Thesis_supervised_by_Lukas_Ifflaender descartes se2_mastersthesis t_studentthesis}, month = {January}, school = {University of W{ü}rzburg}, title = {Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Geiger2019 %A Geiger, Ariane %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context
• 
  Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models. Trapp, Simon; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Trapp2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Trapp, Simon}, keywords = {Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis}, 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
• 
  Mosquitto Swatter: Demonstrating MQTT Vulnerabilities. Metzler, Samuel; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Metzler2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Metzler, Samuel}, keywords = {Cyber-Physical_Systems Industry_4_0 Internet_of_Things Networking Reliability Security Thesis_supervised_by_Lukas_Ifflaender Thesis_supervised_by_SE_member Tool descartes se2_mastersthesis t_studentthesis}, month = {February}, school = {University of W{ü}rzburg}, title = {Mosquitto Swatter: Demonstrating MQTT Vulnerabilities}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Metzler2019 %A Metzler, Samuel %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Mosquitto Swatter: Demonstrating MQTT Vulnerabilities
• 
  An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics. Dreher, Tim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Dreher2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Dreher, Tim}, keywords = {Thesis_supervised_by_Marwin_Zuefle descartes feature_engineering Thesis_supervised_by_SE_member Thesis_supervised_by_Christian_Krupitzer thesis_supervised_by_se_member prediction se2_mastersthesis}, 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
• 
  Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation. Erhard, Florian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Erhard2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Erhard, Florian}, keywords = {Thesis_supervised_by_Marwin_Zuefle feature_engineering Thesis_supervised_by_Christian_Krupitzer prediction descartes Thesis_supervised_by_SE_member statistical_estimation_and_machine_learning se2_mastersthesis}, 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
• 
  Heat-aware and CPU Boost-oriented Server Load Rotation. Knapp, Andreas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Knapp2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Knapp, Andreas}, keywords = {Migration Networking Optimization Performance Power Self-adaptive-systems Thesis_supervised_by_Lukas_Ifflaender Thesis_supervised_by_SE_member Virtualization descartes se2_mastersthesis t_studentthesis}, month = {November}, school = {University of W{ü}rzburg}, title = {Heat-aware and CPU Boost-oriented Server Load Rotation}, type = {{Master Thesis}}, year = 2019 }
  %0 Thesis %1 Knapp2019 %A Knapp, Andreas %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Heat-aware and CPU Boost-oriented Server Load Rotation
• 
  Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. Bauer, André; Herbst, Nikolas; Spinner, Simon; Ali-Eldin, Ahmed; Kounev, Samuel; in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800--813.
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @article{BaHeSpAEKo2019-TPDS-ChameleonAutoscaler, author = {Bauer, Andr{é} and Herbst, Nikolas and Spinner, Simon and Ali-Eldin, Ahmed and Kounev, Samuel}, journal = {IEEE Transactions on Parallel and Distributed Systems}, keywords = {Application-aware Application_quality_of_service_management BUNGEE Chameleon Cloud Elasticity LIMBO Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting PRISMA Performance Prediction Resource_management SPEC Self-adaptive-systems Self-aware-computing Tool descartes t_journalmagazine}, month = {April}, number = 4, pages = {800&ndash;813}, publisher = {IEEE}, title = {{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}}, volume = 30, year = 2019 }
  %0 Journal Article %1 BaHeSpAEKo2019-TPDS-ChameleonAutoscaler %A Bauer, André %A Herbst, Nikolas %A Spinner, Simon %A Ali-Eldin, Ahmed %A Kounev, Samuel %D 2019 %I IEEE %J IEEE Transactions on Parallel and Distributed Systems %N 4 %P 800--813 %T Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field %V 30
• 
  TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research. Eismann, Simon; (2019).
  [ 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 = {Cloud Design_of_software_and_systems Tool se2_talk t_talk SOA descartes}, 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.
• 
  Kaa: Evaluating Elasticity of Cloud-hosted DBMS. Seybold, Daniel; Volpert, Simon; Wesner, Stefan; Bauer, André; Herbst, Nikolas; Domaschka, Jörg; 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 = {Cloud Elasticity Metrics_and_benchmarking_methodologies Performance Tool descartes t_full}, 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
• 
  Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; 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).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{LeKrTo2019, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, booktitle = {Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019}, keywords = {Intelligent_Transportation_Systems Internet_of_Things Multi-criteria_optimization Optimization Self-adaptive-systems descartes t_workshop}, month = {may}, publisher = {Gesellschaft fuer Informatik (GI)}, title = {{Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}}, year = 2019 }
  %0 Conference Paper %1 LeKrTo2019 %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %B Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019 %D 2019 %I Gesellschaft fuer Informatik (GI) %T Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems
• 
  Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads. Tocze, Klervie; Schmitt, Norbert; Brandic, Ivona; Aral, Atakan; Nadjm-Tehrani, Simin; 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 = {Cloud Metrics_and_benchmarking_methodologies Performance Power Power-energy_efficient_computing Resource_management descartes myown t_short}, 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
• 
  Emerging Self-Integration through Coordination of Autonomous Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; in Proceedings of the 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019 (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{LeKrTo2019_SISSY, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, booktitle = {Proceedings of the 4th {IEEE} International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019}, keywords = {Cyber-Physical_Systems Internet_of_Things Self-adaptive-systems Self-aware-computing descartes t_workshop}, month = {June}, publisher = {IEEE}, title = {{Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}}, year = 2019 }
  %0 Conference Paper %1 LeKrTo2019_SISSY %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %B Proceedings of the 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019 %D 2019 %I IEEE %T Emerging Self-Integration through Coordination of Autonomous Adaptive Systems
• 
  Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @misc{talk-Lesch-SelPhyS19, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2019}, keywords = {Cyber-Physical_Systems Internet_of_Things Multi-criteria_optimization Optimization Self-adaptive-systems Talks descartes se2_talk t_talk}, month = {April}, title = {{Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems}}, year = 2019 }
  %0 Generic %1 talk-Lesch-SelPhyS19 %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %D 2019 %T Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems
• 
  A Modular Simulation Framework for Analyzing Platooning Coordination. Krupitzer, Christian; Lesch, Veronika; Pfannemüller, Martin; Becker, Christian; Segata, Michele; 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 = {Analytical_and_simulation-based_analysis Cyber-Physical_Systems Intelligent_Transportation_Systems Internet_of_Things Simulation Tool descartes t_workshop}, 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
• 
  WheelShare: Crowd-sensed Surface Classification for Accessible Routing. Edinger, Janick; Raychoudhury, Vaskar; Hofmann, Alexandra; Wachner, Anton; Becker, Christian; Krupitzer, Christian; in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  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.
  @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 = {descartes Internet_of_Things Self-adaptive-systems Self-aware-computing Optimization}, title = {{WheelShare: Crowd-sensed Surface Classification for Accessible Routing}}, year = 2019 }
  %0 Conference Paper %1 EdRaHoWaBeKr2019-TSUM-wheelShareConcept %A Edinger, Janick %A Raychoudhury, Vaskar %A Hofmann, Alexandra %A Wachner, Anton %A Becker, Christian %A Krupitzer, Christian %B Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) %C Kyoto, Japan %D 2019 %T WheelShare: Crowd-sensed Surface Classification for Accessible Routing %X 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.
• 
  Discrete-Time Analysis of the Blockchain Distributed Ledger Technology. Geissler, Stefan; Prantl, Thomas; Lange, Stanislav; Wamser, Florian; Hossfeld, Tobias; in Proceedings of the 31st International Teletraffic Congress (ITC) (2019).
  [ BibTeX ] [ EndNote ]
   
  @inproceedings{GePrLaWaHo2019-ITC-Blockchain, author = {Geissler, Stefan and Prantl, Thomas and Lange, Stanislav and Wamser, Florian and Hossfeld, Tobias}, booktitle = {Proceedings of the 31st International Teletraffic Congress (ITC)}, keywords = {Security Performance descartes Networking}, month = {August}, title = {Discrete-Time Analysis of the Blockchain Distributed Ledger Technology}, year = 2019 }
  %0 Conference Paper %1 GePrLaWaHo2019-ITC-Blockchain %A Geissler, Stefan %A Prantl, Thomas %A Lange, Stanislav %A Wamser, Florian %A Hossfeld, Tobias %B Proceedings of the 31st International Teletraffic Congress (ITC) %D 2019 %T Discrete-Time Analysis of the Blockchain Distributed Ledger Technology
• 
  A survey on adaptive authentication. Arias-Cabarcos, Patricia; Krupitzer, Christian; Becker, Christian; in ACM Computing Surveys (2019). 52 Art. 80.
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @article{ArKrBe2019_CSUR_surveyAdaptiveAuthentication, address = {New York, NY}, author = {Arias-Cabarcos, Patricia and Krupitzer, Christian and Becker, Christian}, journal = {ACM Computing Surveys}, keywords = {Application-aware descartes Internet_of_Things Self-adaptive-systems Security}, pages = {Art. 80}, publisher = {ACM Press}, title = {A survey on adaptive authentication}, volume = 52, year = 2019 }
  %0 Journal Article %1 ArKrBe2019_CSUR_surveyAdaptiveAuthentication %A Arias-Cabarcos, Patricia %A Krupitzer, Christian %A Becker, Christian %C New York, NY %D 2019 %I ACM Press %J ACM Computing Surveys %P Art. 80 %T A survey on adaptive authentication %U https://madoc.bib.uni-mannheim.de/50229/ %V 52
• 
  Beyond position-awareness -- Extending a self-adaptive fall detection system. Krupitzer, Christian; Sztyler, Timo; Edinger, Janick; Breitbach, Martin; Stuckenschmidt, Heiner; Becker, Christian; in Pervasive and Mobile Computing (2019). 58
  [ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.
  @article{KRUPITZER2019101026, abstract = {Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.}, author = {Krupitzer, Christian and Sztyler, Timo and Edinger, Janick and Breitbach, Martin and Stuckenschmidt, Heiner and Becker, Christian}, journal = {Pervasive and Mobile Computing}, keywords = {Health descartes Internet_of_Things Self-adaptive-systems Cyber-Physical_Systems}, title = {Beyond position-awareness &ndash; Extending a self-adaptive fall detection system}, volume = 58, year = 2019 }
  %0 Journal Article %1 KRUPITZER2019101026 %A Krupitzer, Christian %A Sztyler, Timo %A Edinger, Janick %A Breitbach, Martin %A Stuckenschmidt, Heiner %A Becker, Christian %D 2019 %J Pervasive and Mobile Computing %T Beyond position-awareness -- Extending a self-adaptive fall detection system %U http://www.sciencedirect.com/science/article/pii/S1574119218303493 %V 58 %X Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.
• 
  Towards a QoS-aware Cyber Physical Networking Middleware Architecture. Brinkschulte, Melanie; Becker, Christian; Krupitzer, Christian; in 1st International Workshop on Middleware for Lightweight, Spontaneous Environments (MISE) @ ACM/IFIP/USENIX International Middleware Conference (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{BrBeKr2019-MISE-CPN_Middleware_Vision, address = {Davis, CA, USA}, author = {Brinkschulte, Melanie and Becker, Christian and Krupitzer, Christian}, booktitle = {1st International Workshop on Middleware for Lightweight, Spontaneous Environments (MISE) @ ACM/IFIP/USENIX International Middleware Conference}, keywords = {Cyber-Physical_Systems descartes Networking Self-adaptive-systems Internet_of_Things Design_of_software_and_systems}, title = {{Towards a QoS-aware Cyber Physical Networking Middleware Architecture}}, year = 2019 }
  %0 Conference Paper %1 BrBeKr2019-MISE-CPN_Middleware_Vision %A Brinkschulte, Melanie %A Becker, Christian %A Krupitzer, Christian %B 1st International Workshop on Middleware for Lightweight, Spontaneous Environments (MISE) @ ACM/IFIP/USENIX International Middleware Conference %C Davis, CA, USA %D 2019 %T Towards a QoS-aware Cyber Physical Networking Middleware Architecture
• 
  The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019. Krupitzer, Christian; Tomforde, Sven; in Proceedings of the INFORMATIK 2019 (Workshop-Beiträge) (2019).
  [ BibTeX ] [ EndNote ]
   
  @inproceedings{KrTo2019_Informatik_OC-DDC, author = {Krupitzer, Christian and Tomforde, Sven}, booktitle = {Proceedings of the INFORMATIK 2019 (Workshop-Beitr{ä}ge)}, keywords = {Self-adaptive-systems descartes Internet_of_Things}, organization = {Gesellschaft f{ü}r Informatik eV}, title = {The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019}, year = 2019 }
  %0 Conference Paper %1 KrTo2019_Informatik_OC-DDC %A Krupitzer, Christian %A Tomforde, Sven %B Proceedings of the INFORMATIK 2019 (Workshop-Beiträge) %D 2019 %T The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019
• 
  Artificial Intelligence in Medicine - From Data Collection to Prediction. Züfle, Marwin; Leppich, Robert; (2019).
  [ BibTeX ] [ EndNote ]
   
  @misc{talk-ZuLe, author = {Z{ü}fle, Marwin and Leppich, Robert}, howpublished = {Invited Talk at Klinikum N{ü}rnberg}, keywords = {Cloud Health IoT Prediction Sports Statistical_estimation_and_machine_learning Talks descartes myown t_talk}, 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
• 
  On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. D'Angelo, M.; Gerasimou, S.; Ghahremani, S.; Grohmann, J.; Nunes, I.; Pournaras, E.; Tomforde, S.; in 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2019). 13-24.
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{SEAMS2019-CollectiveLearningSurvey, author = {{D'Angelo}, M. and {Gerasimou}, S. and {Ghahremani}, S. and {Grohmann}, J. and {Nunes}, I. and {Pournaras}, E. and {Tomforde}, S.}, booktitle = {2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, keywords = {Self-adaptive-systems Design_of_software_and_systems t_full Automated_model_learning Self-aware-computing descartes Survey}, month = {may}, pages = {13-24}, 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 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) %D 2019 %P 13-24 %T On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework
• 
  Towards Self-Aware Multirotor Formations. Kaiser, Dennis; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).

  Würzburg Software Engineering Award sponsored by infosim
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @mastersthesis{Kaiser2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Kaiser, Dennis}, keywords = {Cyber-Physical_Systems Design_of_software_and_systems Intelligent_Transportation_Systems Internet_of_Things Self-aware-computing Simulation Thesis_supervised_by_Christian_Krupitzer Thesis_supervised_by_SE_member Thesis_supervised_by_Veronika_Lesch descartes se2_mastersthesis t_studentthesis}, month = {September}, note = {<b>W{ü}rzburg Software Engineering Award sponsored by infosim</b>}, school = {University of W{ü}rzburg}, title = {{Towards Self-Aware Multirotor Formations}}, type = {{Master Thesis}}, year = 2019 }
  %0 Thesis %1 Kaiser2019 %A Kaiser, Dennis %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Towards Self-Aware Multirotor Formations
• 
  A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications. Herrnleben, Stefan; (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @misc{He2019-SelPhyS-Car2Cloud, author = {Herrnleben, Stefan}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2019}, keywords = {Application-aware Internet_of_Things Self-adaptive-systems Talks descartes 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
• 
  SPEC Research Group Newsletter, vol. 2 no. 4. Kounev, Samuel; van Hoorn, Andre; Bauer, André; Herbst, Nikolas; (2019).

  Published by Standard Performance Evaluation Corporation (SPEC)
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @misc{SPEC-RG-NEWSLETTER-8, author = {Kounev, Samuel and van Hoorn, Andre and Bauer, Andr{é} and Herbst, Nikolas}, keywords = {descartes se2 t_other}, month = {April}, note = {Published by Standard Performance Evaluation Corporation (SPEC)}, title = {{SPEC Research Group Newsletter, vol. 2 no. 4}}, year = 2019 }
  %0 Generic %1 SPEC-RG-NEWSLETTER-8 %A Kounev, Samuel %A van Hoorn, Andre %A Bauer, André %A Herbst, Nikolas %D 2019 %T SPEC Research Group Newsletter, vol. 2 no. 4 %U http://research.spec.org/en/newsletter.html
• 
  Best Practices for Time Series Forecasting. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Kounev, Samuel; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{BaZuHeKo2019-ICAC-ForecastingTutorial, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {{2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)}}, keywords = {descartes Telescope t_tutorial Online_monitoring_and_forecasting}, month = {June}, title = {{Best Practices for Time Series Forecasting}}, year = 2019 }
  %0 Conference Paper %1 BaZuHeKo2019-ICAC-ForecastingTutorial %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) %D 2019 %T Best Practices for Time Series Forecasting
• 
  Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. Herbst, Nikolas; (2019).
  [ 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 = {Award BUNGEE Chameleon Cloud Instrumentation_profiling_and_workload_characterization LIMBO Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting Prediction Resource_management Telescope Virtualization descartes se2_talk 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
• 
  Online Power Consumption Estimation for Functions in Cloud Applications. Schmitt, Norbert; Iffländer, Lukas; Bauer, André; Kounev, Samuel; 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 = {Cloud Power Power-energy_efficient_computing descartes myown t_full}, 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. Grohmann, Johannes; Eismann, Simon; Bauer, Andre; Zuefle, Marwin; Herbst, Nikolas; Kounev, Samuel; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134-139.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ 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 = {GoogleResearchAward Instrumentation_profiling_and_workload_characterization LibReDE Optimization PRISMA Performance Self-adaptive-systems Self-aware-computing Statistical_estimation_and_machine_learning descartes t_workshop}, month = {June}, pages = {134-139}, 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 %P 134-139 %T Utilizing Clustering to Optimize Resource Demand Estimation Approaches %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. Kounev, Samuel; (2019).
  [ 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 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
• 
  Hands Off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences Iffländer, Lukas; Dmitrienko, Alexandra; Hagen, Christoph; Jobst, Michael; Kounev, Samuel; (2019).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @techreport{Ifflander_Hands_Off_my_Database, author = {Iffländer, Lukas and Dmitrienko, Alexandra and Hagen, Christoph and Jobst, Michael and Kounev, Samuel}, institution = {Universit{ä}t W{ü}rzburg}, journal = {arXiv}, keywords = {Design_of_software_and_systems EvIDencE Performance Security descartes myown t_techreport}, month = {jul}, title = {Hands Off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences}, year = 2019 }
  %0 Report %1 Ifflander_Hands_Off_my_Database %A Iffländer, Lukas %A Dmitrienko, Alexandra %A Hagen, Christoph %A Jobst, Michael %A Kounev, Samuel %D 2019 %J arXiv %T Hands Off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences %U https://arxiv.org/abs/1907.06775
• 
  From the Internet-of-Things to the Internet-of-Thinking-Things. Kounev, Samuel; (2019).
  [ 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 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
• 
  Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques. Grohmann, Johannes; Eismann, Simon; Elflein, Sven; Mazkatli, Manar; von Kistowski, Jóakim; Kounev, Samuel; 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 = {Automated_model_learning DML PRISMA Performance Prediction Self-aware-computing Statistical_estimation_and_machine_learning descartes t_full}, 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. Beierlieb, Lukas; Iffländer, Lukas; Milenkoski, Aleksandar; Goncalves, Charles F.; Antunes, Nuno; Kounev, Samuel; in 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (2019).
  [ Abstract ] [ BibTeX ] [ EndNote ] [