piwik-script

Deutsch Intern
    Chair of Computer Science II - Software Engineering

    Publications

    Publications of Software-Engineering Group / Descartes Research Group

    This list contains dynamically generated, up-to-date list of publications with filtering possibilities.
    Show publications grouped by research areas.

    2022[ to top ]
    • MiSim: A Simulator for Resilience Assessment of Microservice-based Architectures. S. Frank; L. Wagner; A. Hakamian; M. Straesser; A. van Hoorn; in Proceedings of the IEEE 22nd International Conference on Software Quality, Reliability, and Security (QRS~2022) (2022).
      In print.
    • SPEC Efficiency Benchmark...
      SPEC Efficiency Benchmark Development: How to Contribute to the Future of Energy Conservation. M. Meissner; K.-D. Lange; J. Arnold; S. Sharma; R. Tipley; N. Rawtani; D. Reiner; M. Petrich; A. Cragin; in Companion of the 2022 ACM/SPEC International Conference on Performance Engineering (2022).
    • Towards Forecasting Future Snow Cover Dynamics in the European Alps - The Potential of Long Optical Remote-Sensing Time Series. J. Koehler; A. Bauer; A. J. Dietz; C. Kuenzer; in Remote Sensing (2022). 14(18)
    • Experience and Guidelines for Sorting Algorithm Choices and Their Energy Efficiency. M. Meissner; S. Kamthania; N. Rawtani; J. Bucek; K.-D. Lange; S. Kounev; in Companion of the 2022 ACM/SPEC International Conference on Performance Engineering (2022).
    • Self-Aware Optimization of Adaptation Planning Strategies. V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; in ACM Transactions on Autonomous and Adaptive Systems (2022).
    • CortexVR: Immersive analy...
      CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning. C. Krupitzer; J. Naber; J.-P. Stauffert; J. Mayer; J. Spielmann; P. Ehmann; N. Boci; M. Bürkle; A. Ho; C. Komorek; F. Heinickel; S. Kounev; C. Becker; M. E. Latoschik; in Frontiers in Psychology (2022). 5139.
    • Leveraging Kubernetes Source Code for Performance Simulation M. Straesser; P. Haas; (2022).
    • Measurement, Modeling, and Emulation of Power Consumption of Distributed Systems. N. Schmitt; (2022, Juni).
    • A Survey on Secure Group Communication Schemes with Focus on IoT Communication. T. Prantl; T. Zeck; A. Bauer; P. Ten; D. Prantl; A. E. Ben Yahya; L. Iffländer; A. Dmitrienko; C. Krupitzer; S. Kounev; in IEEE Access (2022).
      Accepted but not yet published
    • Ransomware Detection in Databases through Dynamic Analysis of Query Sequences. C. Sendner; L. Iffländer; S. Schindler; M. Jobst; A. Dmitrienko; S. Kounev; in 2022 IEEE Conference on Communications and Network Security (CNS) (2022).
    • SPEC Research - Introducing the Predictive Data Analytics Working Group. A. Bauer; M. Leznik; D. Seybold; I. Trubin; B. Erb; J. Domaschka; P. Jamshidi; in Companion of the 2022 ACM/SPEC International Conference on Performance Engineering (2022).
    • A literature review on op...
      A literature review on optimization techniques for adaptation planning in adaptive systems: State of the art and research directions. E. Henrichs; V. Lesch; M. Straesser; S. Kounev; C. Krupitzer; in Information and Software Technology (2022). 149 106940.
    • Change Point Detection for MongoDB Time Series Performance Regression. M. Leznik; M. S. Iqbal; I. A. Trubin; A. Lochner; P. Jamshidi; A. Bauer; in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022).
    • Self-Aware Optimization of Cyber-Physical Systems in Intelligent Transportation and Logistics Systems. V. Lesch; Thesis; Universität Würzburg. (2022, April).
    • An Experience Report on the Suitability of a Distributed Group Encryption Scheme for an IoT Use Case. T. Prantl; S. Engel; A. Bauer; A. E. Ben Yahya; S. Herrnleben; L. Iffländer; A. Dmitrienko; S. Kounev; in 2022 IEEE 95th Vehicular Technology Conference (VTC2022) (2022).
    • FADE: Towards Flexible an...
      FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles. M. Hadry; V. Lesch; S. Kounev; in Companion of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE 2022) (2022).
    • Automated Triage of Perfo...
      Automated Triage of Performance Change Points Using Time Series Analysis and Machine Learning. A. Bauer; M. Straesser; L. Beierlieb; M. Meissner; S. Kounev; in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022).
    • SPEC Research - Introduci...
      SPEC Research - Introducing the Predictive Data Analytics Working Group. A. Bauer; M. Leznik; D. Seybold; I. Trubin; B. Erb; J. Domaschka; P. Jamshidi; in Companion of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022).
    • Same, Same, but Dissimila...
      Same, Same, but Dissimilar: Exploring Measurements for Workload Time-Series Similarity. M. Leznik; J. Grohmann; N. Kliche; A. Bauer; D. Seybold; S. Eismann; S. Kounev; J. Domaschka; in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022). 89–96.
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, Issue 11. S. Kounev; A. van Hoorn; M. Straesser; A. Bauer; (2022, April).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • Why Is It Not Solved Yet?...
      Why Is It Not Solved Yet? Challenges for Production-Ready Autoscaling. M. Straesser; J. Grohmann; J. von Kistowski; S. Eismann; A. Bauer; S. Kounev; in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022). 105–115.
    • A case study on the stabi...
      A case study on the stability of performance tests for serverless applications. S. Eismann; D. Costa; L. Liao; C.-P. Bezemer; W. Shang; A. van Hoorn; S. Kounev; in Journal of Systems and Software (JSS) (2022). 189
    • Model Learning for Perfor...
      Model Learning for Performance Prediction of Cloud-native Microservice Applications. J. Grohmann; Thesis; Universität Würzburg. (2022, März).
      Distinction
    • Towards a Cryptography Benchmark: A View on Attribute Based Encryption Schemes. T. Prantl; T. Zeck; L. Iffländer; L. Beierlieb; A. Dmitrenko; C. Krupitzer; S. Kounev; in 2022 5th Conference on Cloud and Internet of Things (CIoT) (2022).
    • A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation. F. Spiess; N. Strobel; T. Kaupp; S. Kounev; in Proceedings of the IEEE IRC 2022 Conference (2022).
    2021[ to top ]
    • Performance Evaluation of a Post-Quantum Public-Key Cryptosystem. T. Prantl; D. Prantl; L. Beierlieb; L. Iffländer; A. Dmitrienko; C. Krupitzer; S. Kounev; in 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
    • Measuring the Performance Impact of Branching Instructions. L. Beierlieb; L. Iffländer; A. Milenkoski; T. Prantl; S. Kounev; in Proceedings of the 12th Symposium on Software Performance 2021 (SSP’21) (2021).
    • Teaching Software Testing Using Automated Grading. L. Beierlieb; L. Iffländer; T. Schneider; T. Prantl; S. Kounev; in Proceedings of the Fifth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2021),virtual event, October 28-29, 2021, A. Greubel, S. Strickroth, M. Striewe (Hrsg.) (2021).
    • Software Testing Strategies for Detecting Hypercall Handlerstextquotesingle Aging-related Bugs. L. Beierlieb; A. Avritzer; L. Ifflander; N. Antunes; A. Milenkoski; S. Kounev; in 2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (2021).
    • SPEC - Spotlight on the International Standards Group (ISG). N. Schmitt; K.-D. Lange; S. Sharma; A. Cragin; D. Reiner; S. Kounev; in Companion of the ACM/SPEC International Conference on Performance Engineering (2021).
    • An Overview on Approaches...
      An Overview on Approaches for Coordination of Platoons. V. Lesch; M. Breitbach; M. Segata; C. Becker; S. Kounev; C. Krupitzer; in IEEE Transactions on Intelligent Transportation Systems (2021).
      Early Access on IEEE Xplore
    • Attack-aware Security Fun...
      Attack-aware Security Function Management. L. Iffländer; Thesis; Universität Würzburg. (2021, Januar).
    • Automatisierte Hybride Zeitreihenprognose. A. Bauer; in Ausgezeichnete Informatikdissertationen 2020, S. Hölldobler (Hrsg.) (2021). 29–38.
    • People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot. F. Spiess; L. Reinhart; N. Strobel; D. Kaiser; S. Kounev; T. Kaupp; in Journal of Image and Graphics, (J. of Image; Graphics, Hrsg.) (2021). 9(4) 135–139.
    • Towards Splitting Monolithic Workflows into Serverless Functions and Estimating Their Run-Time in the Earth Observation Domain. D. Kaiser; B. Dovhan; A. Bauer; S. Kounev; in Proceedings of the 12th Symposium on Software Performance (SSP) (2021).
      Extended Abstract
    • Energy-Efficiency Comparison of Common Sorting Algorithms. N. Schmitt; S. Kamthania; N. Rawtani; L. Mendoza; K.-D. Lange; S. Kounev; in 2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2021). 1–8.
    • Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment. F. Spiess; J. Friesslich; D. Bluemm; F. Mast; D. Vinokour; S. Kounev; T. Kaupp; N. Strobel; in 2021 20th International Conference on Advanced Robotics (ICAR) (2021). 857–864.
    • Tackling the Rich Vehicle Routing Problem with Nature-Inspired Algorithms. V. Lesch; M. König; S. Kounev; A. Stein; C. Krupitzer; in Applied Intelligence (2021). 52 9476–9500.
    • Serverless Computing (Dagstuhl Seminar 21201). C. Abad; I. T. Foster; N. Herbst; A. Iosup; in Dagstuhl Reports, (C. Abad; I. T. Foster; N. Herbst; A. Iosup, Hrsg.) (2021). 11(4) 34–93.
    • A Case Study on Optimizat...
      A Case Study on Optimization of Platooning Coordination V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; (2021).
    • A Case Study of Vehicle R...
      A Case Study of Vehicle Route Optimization V. Lesch; M. König; S. Kounev; A. Stein; C. Krupitzer; (2021).
    • Container Start Times: Em...
      Container Start Times: Empirical Analysis and Predictability M. Straesser; (2021).
    • A Case Study on Optimizat...
      A Case Study on Optimization of Warehouses V. Lesch; P. B. Müller; M. Krämer; S. Kounev; C. Krupitzer; (2021).
    • Sizeless: Predicting the ...
      Sizeless: Predicting the Optimal Size of Serverless Functions. S. Eismann; L. Bui; J. Grohmann; C. Abad; N. Herbst; S. Kounev; in Proceedings of the 22nd International MIDDLEWARE Conference (2021). 248–259.
      Best Student Paper Award, ACM Artifacts Evaluated — Functional
    • ComBench: A Benchmarking...
      ComBench: A Benchmarking Framework for Publish/Subscribe Communication Protocols Under Network Limitations. S. Herrnleben; M. Leidinger; V. Lesch; T. Prantl; J. Grohmann; C. Krupitzer; S. Kounev; in Performance Evaluation Methodologies and Tools, Q. Zhao, L. Xia (Hrsg.) (2021). 72–92.
    • Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction. M. Züfle; F. Erhard; S. Kounev; in 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2021).
      (to appear)
    • A Predictive Maintenance ...
      A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0. M. Züfle; J. Agne; J. Grohmann; I. Dörtoluk; S. Kounev; in Proceedings of the 21st IEEE IES International Conference on Industrial Informatics (2021).
    • Utility-based Vehicle Rou...
      Utility-based Vehicle Routing Integrating User Preferences. V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; in Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021 (2021).
    • Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT. T. Prantl; D. Prantl; A. Bauer; L. Iffländer; A. Dmitrenko; C. Krupitzer; S. Kounev; in 2021 IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
    • Towards Situation-Aware M...
      Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies. V. Lesch; T. Noack; J. Hefter; S. Kounev; C. Krupitzer; in Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021 (2021).
      Best Paper Award Runner Up, Full Paper Acceptance Ratio: 25%
    • Auswirkungen der COVID-19-Pandemie auf Sportlerinnen und Sportler in Deutschland. C. Zinner; M. Matzka; R. Leppich; S. Kounev; H.-C. Holmberg; B. Sperlich; in 53. asp Jahrestagung (2021).
    • A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools. M. Züfle; F. Moog; V. Lesch; C. Krupitzer; S. Kounev; in ISA Transactions: The Journal of Automation (2021).
      (in press)
    • Establishing a cardiac training group for patients with heart failure: the “HIP-in-Würzburg” study. G. Güder; J. Wilkesmann; N. Scholz; R. Leppich; P. Düking; B. Sperlich; C. Rost; S. Frantz; C. Morbach; F. Sahiti; others; in Clinical Research in Cardiology (2021). 1–10.
    • Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping. N. Finke; M. Mohr; A. Lontke; M. Züfle; S. Kounev; R. Möller; in Research Challenges in Information Science (RCIS) (2021). 189–204.
    • SARDE: A Framework for Co...
      SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation. J. Grohmann; S. Eismann; A. Bauer; S. Spinner; J. Blum; N. Herbst; S. Kounev; in ACM Transactions on Autonomous and Adaptive Systems (2021). 15(2)
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, vol. 3 no. 2. S. Kounev; A. van Hoorn; A. Bauer; (2021, April).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • The integration of training and off-training activities substantially alters training volume and load analysis in elite rowers. G. Treff; R. Leppich; K. Winkert; J. M. Steinacker; B. Mayer; B. Sperlich; in Scientific reports (2021). 11(1) 1–10.
    • Buzzy: Towards Realistic ...
      Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads. J. Domaschka; S. Eismann; M. Leznik; J. Grohmann; S. Kounev; D. Seybold; in Companion of the ACM/SPEC International Conference on Performance Engineering (2021). (Bd. ICPE ’21) 175–178.
    • SuanMing: Explainable Pre...
      SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications. J. Grohmann; M. Straesser; A. Chalbani; S. Eismann; Y. Arian; N. Herbst; N. Peretz; S. Kounev; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
      Acceptance Rate: 29%
    • The State of Serverless A...
      The State of Serverless Applications: Collection, Characterization, and Community Consensus. S. Eismann; J. Scheuner; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; in Transactions on Software Engineering (2021).
    • Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT. T. Prantl; P. Ten; L. Iffländer; S. Herrnleben; A. Dmitrenko; S. Kounev; C. Krupitzer; in 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
    • Performance Impact Analysis of Securing MQTT Using TLS. T. Prantl; L. Iffländer; S. Herrnleben; S. Engel; S. Kounev; C. Krupitzer; in 2021 ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
    • Libra: A Benchmark for Ti...
      Libra: A Benchmark for Time Series Forecasting Methods. A. Bauer; M. Züfle; S. Eismann; J. Grohmann; N. Herbst; S. Kounev; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
    • A Comparison of Mechanism...
      A Comparison of Mechanisms for Compensating Negative Impacts of System Integration. V. Lesch; C. Krupitzer; K. Stubenrauch; N. Keil; C. Becker; S. Kounev; M. Segata; in Future Generation Computer Systems (2021). 116 117–131.
    • Serverless Applications:W...
      Serverless Applications:Why, When, and How?. S. Eismann; S. Joel; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; in IEEE Software (2021). 38(1) 32–39.
    • A Simulation-based Optimi...
      A Simulation-based Optimization Framework for Online Adaptation of Networks. S. Herrnleben; J. Grohmann; P. Rygielski; V. Lesch; C. Krupitzer; S. Kounev; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools), H. Song, D. Jiang (Hrsg.) (2021). 513–532.
    2020[ to top ]
    • Microservices: A Performa...
      Microservices: A Performance Tester’s Dream or Nightmare?. S. Eismann; C.-P. Bezemer; W. Shang; D. Okanovic; A. van Hoorn; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020).
      Acceptance Rate: 23.4% (15/64), ACM Artifacts Evaluated — Functional
    • Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data. F. Spiess; J. Friesslich; K. Tobias; S. Kounev; N. Strobel; in International Journal of Mechanical Engineering and Robotics Research (IJMERR), (P. R. (Chunhui) Yang, Hrsg.) (2020). 9(12) 1532–1540.
    • Learning to Learn in Coll...
      Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning. M. D’Angelo; S. Ghahremani; S. Gerasimou; J. Grohmann; I. Nunes; S. Tomforde; E. Pournaras; in 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (2020). 121–126.
    • Systems Benchmarking S. Kounev; K.-D. Lange; J. von Kistowski; (2020). (1. Aufl.) Springer International Publishing.
    • An Overview of Design Pat...
      An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things. C. Krupitzer; T. Temizer; T. Prantl; C. Raibulet; in IEEE Access (2020).
    • REACT: A Model-Based Runt...
      REACT: A Model-Based Runtime Environment for Adapting Communication Systems. M. Pfannemüller; M. Breitbach; C. Krupitzer; M. Weckesser; C. Becker; B. Schmerl; A. Schürr; in Proceedings of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020) (2020).
    • A Framework for Time Seri...
      A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation. M. Züfle; S. Kounev; in Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications (2020).
    • Predicting Performance De...
      Predicting Performance Degradations of Black-box Microservice Applications M. Straesser; (2020).
    • Elasticity of Cloud Platforms. N. Herbst; A. Bauer; S. Kounev; in Systems Benchmarking: For Scientists and Engineers, S. Kounev, K.-D. Lange, J. von Kistowski (Hrsg.) (2020). 319–340.
    • Performance, Power, and Energy-Efficiency Impact Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite. N. Schmitt; J. Bucek; J. Beckett; A. Cragin; K.-D. Lange; S. Kounev; in 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) (2020). 292–301.
    • An IoT Network Emulator f...
      An IoT Network Emulator for Analyzing the Influence of Varying Network Quality. S. Herrnleben; R. Ailabouni; J. Grohmann; T. Prantl; C. Krupitzer; S. Kounev; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) (2020).
    • Incremental Calibration o...
      Incremental Calibration of Architectural Performance Models with Parametric Dependencies. M. Mazkatli; D. Monschein; J. Grohmann; A. Koziolek; in 2020 IEEE International Conference on Software Architecture (ICSA 2020) (2020). 23–34.
    • Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study. P. Düking; H.-C. Holmberg; P. Kunz; R. Leppich; B. Sperlich; in European Journal of Applied Physiology (2020). 1–9.
    • Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario. T. Prantl; P. Ten; L. Iffländer; A. Dmitrenko; S. Kounev; C. Krupitzer; in 2020 IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020).
      Acceptance Rate: 27%
    • The impact of the German strategy for containment of Coronavirus SARS-CoV-2 on the training characteristics, physical activity, sleep of highly trained kayakers and canoeists: A retrospective observational study. C. Zinner; M. Matzka; R. Leppich; S. Kounev; H. Holmberg; B. Sperlich; in Frontiers in Sports and Active Living (2020). 2 127.
    • SIMPL: Secure IoT Managem...
      SIMPL: Secure IoT Management Platform. T. Prantl; A. E. Ben Yahya; A. Dmitrienko; S. Kounev; F. Lipp; D. Hock; C. Rathfelder; M. Hofherr; in ITG Workshop on IT Security (ITSec) (2020).
    • Quantifying measurement q...
      Quantifying measurement quality and load distribution in Tor. A. Greubel; S. Pohl; S. Kounev; in Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC 2020) (2020).
    • Baloo: Measuring and Mode...
      Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS. J. Grohmann; D. Seybold; S. Eismann; M. Leznik; S. Kounev; J. Domaschka; in 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020). 1–8.
      Acceptance Rate: 27%
    • A Concept for Crowd-sensed Prediction of Mobile Network Connectivity. S. Herrnleben; B. Zeidler; M. Züfle; C. Krupitzer; S. Kounev; in GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 (2020).
    • Predicting the Costs of S...
      Predicting the Costs of Serverless Workflows. S. Eismann; J. Grohmann; E. van Eyk; N. Herbst; S. Kounev; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020). 265–276.
      Acceptance Rate: 23.4% (15/64)
    • Introduction to the Speci...
      Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation’’. C. Krupitzer; B. Eberhardinger; I. Gerostathopoulos; C. Raibulet; (2020).
    • An Automated Forecasting ...
      An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. A. Bauer; M. Züfle; J. Grohmann; N. Schmitt; N. Herbst; S. Kounev; in Proceedings of the ACM/SPEC International Conference on Performance Engineering (2020). 48–55.
    • Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press. J. Agne; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, März).
    • Towards a Self-Aware Prediction of Critical States. M. Züfle; in Organic Computing: Doctoral Dissertation Colloquium 2020, S. Tomforde, C. Krupitzer (Hrsg.) (2020).
      (to appear)
    • Toward a Framework for Self-Learning Adaptation Planning through Optimization. V. Lesch; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, vol. 3 no. 1. S. Kounev; A. van Hoorn; A. Bauer; N. Herbst; (2020, April).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • A Review of Serverless Us...
      A Review of Serverless Use Cases and their Characteristics S. Eismann; J. Scheuner; E. van Eyk; M. Schwinger; J. Grohmann; N. Herbst; C. Abad; A. Iosup; (2020).
    • Methodological Principles...
      Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. L. Abad; J. N. Amaral; P. Tuma; A. Iosup; in Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria, M. Felderer, W. Hasselbring, R. Rabiser, R. Jung (Hrsg.) (2020). (Bd. P-300) 93–94.
    • Beyond Microbenchmarks: T...
      Beyond Microbenchmarks: The SPEC-RG Vision for A Comprehensive Serverless Benchmark. E. van Eyk; J. Scheuner; S. Eismann; C. Abad; A. Iosup; in Companion of the 2020 ACM/SPEC International Conference on Performance Engineering (2020).
    • Time Series Forecasting for Self-Aware Systems. A. Bauer; M. Züfle; N. Herbst; A. Zehe; A. Hotho; S. Kounev; in Proceedings of the IEEE (2020). 108(7) 1068–1093.
    • Model-based Performance P...
      Model-based Performance Predictions for SDN-based Networks: A Case Study. S. Herrnleben; P. Rygielski; J. Grohmann; S. Eismann; T. Hossfeld; S. Kounev; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020).
    • A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. J. Grohmann; N. Herbst; A. Chalbani; Y. Arian; N. Peretz; S. Kounev; in Computers (2020). 9(1) 10.
    • Energy Efficiency Analysi...
      Energy Efficiency Analysis of Compiler Optimizations on the SPEC CPU 2017 Benchmark Suite. N. Schmitt; J. Bucek; K.-D. Lange; S. Kounev; in Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) (2020).
    • A Survey on Predictive Ma...
      A Survey on Predictive Maintenance for Industry 4.0 C. Krupitzer; T. Wagenhals; M. Züfle; V. Lesch; D. Schäfer; A. Mozaffarin; J. Edinger; C. Becker; S. Kounev; (2020).
    • To Fail Or Not To Fail: P...
      To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows. M. Züfle; C. Krupitzer; F. Erhard; J. Grohmann; S. Kounev; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020). 19–36.
    • Telescope: An Automatic F...
      Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. A. Bauer; M. Züfle; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
    • A Survey on Human Machine...
      A Survey on Human Machine Interaction in Industry 4.0 C. Krupitzer; S. Müller; V. Lesch; M. Züfle; J. Edinger; A. Lemken; D. Schäfer; S. Kounev; C. Becker; (2020).
    • Towards Self-Aware Multir...
      Towards Self-Aware Multirotor Formations. D. Kaiser; V. Lesch; J. Rothe; M. Strohmeier; F. Spiess; C. Krupitzer; S. Montenegro; S. Kounev; in Computers (2020). 9(7)
      Special Issue on Self-Aware Computing
    • Optimizing Parametric Dep...
      Optimizing Parametric Dependencies for Incremental Performance Model Extraction. S. Voneva; M. Mazkatli; J. Grohmann; A. Koziolek; in Companion of the 14th European Conference Software Architecture (ECSA 2020), H. Muccini, P. Avgeriou, B. Buhnova, J. Cámara, M. Caporuscio, M. Franzago, A. Koziolek, P. Scandurra, C. Trubiani, D. Weyns, U. Zdun (Hrsg.) (2020). (Bd. 1269) 228–240.
    2019[ to top ]
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, vol. 2 no. 4. S. Kounev; A. van Hoorn; A. Bauer; N. Herbst; (2019, April).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • Artificial Intelligence in Medicine - From Data Collection to Prediction. M. Züfle; R. Leppich; (2019, November).
    • Monitorless: Predicting P...
      Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning. J. Grohmann; P. K. Nicholson; J. O. Iglesias; S. Kounev; D. Lugones; in Proceedings of the 20th International Middleware Conference (2019). 149–162.
    • Best Practices for Time S...
      Best Practices for Time Series Forecasting. A. Bauer; M. Züfle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019).
    • Agile Scalability Engineering: The ScrumScale Method. G. Brataas; G. Hanssen; N. Herbst; A. van Hoorn; in IEEE Software (2019). 37(5) 77–85.
      to appear
    • Methoden und Messverfahre...
      Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. N. Herbst; (2019, Juni).
    • Kaa: Evaluating Elasticit...
      Kaa: Evaluating Elasticity of Cloud-hosted DBMS. D. Seybold; S. Volpert; S. Wesner; A. Bauer; N. Herbst; J. Domaschka; in Proceedings of the 11th IEEE International Conference on Cloud Computing (CloudCom) (2019).
    • Modeling of Aggregated Io...
      Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud. F. Metzger; T. Hoßfeld; A. Bauer; S. Kounev; P. E. Heegaard; in Proceedings of the IEEE (2019). 107(4) 679–694.
    • TeaStore - A Micro-Servic...
      TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research. S. Eismann; (2019, März).
    • Integrating Statistical R...
      Integrating Statistical Response Time Models in Architectural Performance Models. S. Eismann; J. Grohmann; J. Walter; J. von Kistowski; S. Kounev; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80.
      Acceptance Rate: 21,9% (21/96)
    • Performance Influence of ...
      Performance Influence of Security Function Chain Ordering. L. Iffländer; N. Fella; in Companion of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45–46.
    • Die Mehrbelastung der Sicherheit im IoT mit neuen Techniken bewältigen. J. Stoll; L. Iffländer; (2019, April).
    • The SPEC-RG Reference Arc...
      The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms. E. van Eyk; J. Grohmann; S. Eismann; A. Bauer; L. Versluis; L. Toader; N. Schmitt; N. Herbst; C. L. Abad; A. Iosup; in IEEE Internet Computing (2019). 23(6) 7–18.
    • Planning as Optimization:...
      Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations. E. M. Fredericks; I. Gerostathopoulos; C. Krupitzer; T. Vogel; in Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (2019).
    • Methodological Principles...
      Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. Abad; J. N. Amaral; P. Tuma; A. Iosup; in IEEE Transactions on Software Engineering (2019). 47(8) 1528–1543.
    • Towards Testing the Perfo...
      Towards Testing the Performance Influence of Hypervisor Hypercall Interface Behavior. L. Beierlieb; L. Iffländer; S. Kounev; A. Milenkoski; in Proceedings of the 10th Symposium on Software Performance 2019 (SSP’19) (2019).
    • Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models. S. Trapp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Dezember).
    • An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics. T. Dreher; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Oktober).
    • Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation. F. Erhard; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, September).
    • How is Performance Addres...
      How is Performance Addressed in DevOps?. C.-P. Bezemer; S. Eismann; V. Ferme; J. Grohmann; R. Heinrich; P. Jamshidi; W. Shang; A. van Hoorn; M. Villavicencio; J. Walter; F. Willnecker; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45–50.
    • Measuring and Rating the Energy-Efficiency of Servers. J. von Kistowski; K.-D. Lange; J. A. Arnold; J. Beckett; H. Block; M. Tricker; S. Sharma; J. Pais; S. Kounev; in Future Generation Computer Systems (2019). 100 579–589.
    • Online model learning for...
      Online model learning for self-aware computing infrastructures. S. Spinner; J. Grohmann; S. Eismann; S. Kounev; in Journal of Systems and Software (2019). 147 1–16.
    • Chameleon: A Hybrid, Proa...
      Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; N. Herbst; S. Spinner; A. Ali-Eldin; S. Kounev; in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.
    • Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen. L. Hick; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, März).
    • Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report. (SPEC-RG-2019-04), A. V. Papadopoulos; L. Versluis; A. Bauer; N. Herbst; J. von Kistowski; A. Ali-Eldin; C. Abad; J. N. Amaral; P. Tuma; A. Iosup; (2019).
    • Predicting Server Power C...
      Predicting Server Power Consumption from Standard Rating Results. J. von Kistowski; J. Grohmann; N. Schmitt; S. Kounev; in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019). 301–312.
      Full Paper Acceptance Rate: 18.6% (13/70)
    • Measuring the Energy Effi...
      Measuring the Energy Efficiency of Transactional Loads on GPGPU. J. von Kistowski; J. Pais; T. Wahl; K.-D. Lange; H. Block; J. Beckett; S. Kounev; in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019).
      Best Industry Paper Award
    • Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. J. C. Walter; Thesis; Universität Würzburg. (2019).
    • Challenges and Approaches...
      Challenges and Approaches: Forecasting for Autonomic Computing. A. Bauer; in Organic Computing: Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (Hrsg.) (2019).
    • Towards Testing the Softw...
      Towards Testing the Software Aging Behavior of Hypervisor Hypercall Interfaces. L. Beierlieb; L. Iffländer; A. Milenkoski; C. F. Goncalves; N. Antunes; S. Kounev; in 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (2019).
    • WheelShare: Crowd-sensed ...
      WheelShare: Crowd-sensed Surface Classification for Accessible Routing. J. Edinger; V. Raychoudhury; A. Hofmann; A. Wachner; C. Becker; C. Krupitzer; in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019).