piwik-script

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 ]
    • 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)
    • 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)
    • 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).
    • Self-Aware Optimization of Cyber-Physical Systems in Intelligent Transportation and Logistics Systems. V. Lesch; Thesis; Universität Würzburg. (2022, April).
    • 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).
    • 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).
    • 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).
    • 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.
    • 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).
    • 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).
    2021[ to top ]
    • {SPEC Research Group News...
      {SPEC Research Group Newsletter, vol. 3 no. 2}. S. Kounev; A. van Hoorn; A. Bauer; (2021, April).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • 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.
    • 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.
    • 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).
    • Attack-aware Security Fun...
      Attack-aware Security Function Management. L. Iffl{\"a}nder; Thesis; Universit{\"a}t W{\"u}rzburg. (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
    • 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.
    • 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.
    • 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.
    • A Case Study on Optimizat...
      A Case Study on Optimization of Platooning Coordination V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; (2021).
    • Container Start Times: Em...
      Container Start Times: Empirical Analysis and Predictability M. Straesser; (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
    • Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction. M. Z{\"u}fle; F. Erhard; S. Kounev; in 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2021).
      (to appear)
    • {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.
    • 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
    • A Case Study on Optimizat...
      A Case Study on Optimization of Warehouses V. Lesch; P. B. Müller; M. Krämer; S. Kounev; C. Krupitzer; (2021).
    • 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.
    • 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%
    • 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.
    • 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)
    • Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping. N. Finke; M. Mohr; A. Lontke; M. Z{\"u}fle; S. Kounev; R. M{\"o}ller; in Research Challenges in Information Science (RCIS) (2021). 189–204.
    • Establishing a cardiac training group for patients with heart failure: the “HIP-in-W{\"u}rzburg” study. G. G{\"u}der; J. Wilkesmann; N. Scholz; R. Leppich; P. D{\"u}king; B. Sperlich; C. Rost; S. Frantz; C. Morbach; F. Sahiti; others; in Clinical Research in Cardiology (2021). 1–10.
    • 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).
    • 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)
    • 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).
    • 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).
    • 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%
    • 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).
    • 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).
    • 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).
    • {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).
    • 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.
    • 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).
    • Libra: A Benchmark for Ti...
      Libra: A Benchmark for Time Series Forecasting Methods. A. Bauer; M. Z{\"u}fle; 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.
    • A Case Study of Vehicle R...
      A Case Study of Vehicle Route Optimization V. Lesch; M. König; S. Kounev; A. Stein; C. Krupitzer; (2021).
    2020[ to top ]
    • {A Concept for Crowd-sensed Prediction of Mobile Network Connectivity}. S. Herrnleben; B. Zeidler; M. Z{\"u}fle; C. Krupitzer; S. Kounev; in GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 (2020).
    • Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study. P. D{\"u}king; H.-C. Holmberg; P. Kunz; R. Leppich; B. Sperlich; in European Journal of Applied Physiology (2020). 1–9.
    • 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.
    • 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.
    • 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\%
    • 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.
    • 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. Z{\"u}fle; 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).
    • 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).
    • {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.
    • 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. C{\’{a}}mara, M. Caporuscio, M. Franzago, A. Koziolek, P. Scandurra, C. Trubiani, D. Weyns, U. Zdun (Hrsg.) (2020). (Bd. 1269) 228–240.
    • 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%
    • {Systems Benchmarking} S. Kounev; K.-D. Lange; J. von Kistowski; (2020). (1. Aufl.) Springer International Publishing.
    • 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).
    • {To Fail Or Not To Fail: ...
      {To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows}. M. Z{\"u}fle; 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.
    • 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).
    • {A Survey on Predictive M...
      {A Survey on Predictive Maintenance for Industry 4.0} C. Krupitzer; T. Wagenhals; M. Z{\"u}fle; V. Lesch; D. Sch{\"a}fer; A. Mozaffarin; J. Edinger; C. Becker; S. Kounev; (2020).
    • {Methodological Principle...
      {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.
    • Towards a Self-Aware Prediction of Critical States. M. Z{\"u}fle; in Organic Computing: Doctoral Dissertation Colloquium 2020, S. Tomforde, C. Krupitzer (Hrsg.) (2020).
      (to appear)
    • {A Survey on Human Machin...
      {A Survey on Human Machine Interaction in Industry 4.0} C. Krupitzer; S. M{\"u}ller; V. Lesch; M. Z{\"u}fle; J. Edinger; A. Lemken; D. Sch{\"a}fer; S. Kounev; C. Becker; (2020).
    • {SPEC Research Group News...
      {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).
    • Toward a Framework for Self-Learning Adaptation Planning through Optimization. V. Lesch; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).
    • {Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press}. J. Agne; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, Februar).
    • {Energy Efficiency Analys...
      {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 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.
    • {An Automated Forecasting...
      {An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series}. A. Bauer; M. Z{\"u}fle; J. Grohmann; N. Schmitt; N. Herbst; S. Kounev; in Proceedings of the ACM/SPEC International Conference on Performance Engineering (2020). 48–55.
    • 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)}
    • {Beyond Microbenchmarks: ...
      {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).
    • {Model-based Performance ...
      {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).
    • {Towards Self-Aware Multi...
      {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
    • {Introduction to the Spec...
      {Introduction to the Special Issue ``Applications in Self-Aware Computing Systems and their Evaluation’’}. C. Krupitzer; B. Eberhardinger; I. Gerostathopoulos; C. Raibulet; (2020).
    • Telescope: An Automatic F...
      Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. A. Bauer; M. Z{\"u}fle; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
    • {Microservices: A Perform...
      {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}
    • {Time Series Forecasting for Self-Aware Systems}. A. Bauer; M. Z{\"u}fle; N. Herbst; A. Zehe; A. Hotho; S. Kounev; in Proceedings of the IEEE (2020). 108(7) 1068–1093.
    • 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.
    • 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.
    2019[ to top ]
    • {Improving the Energy Eff...
      {Improving the Energy Efficiency of IoT-Systems and its Software}. N. Schmitt; in {Organic Computing: Doctoral Dissertation Colloquium 2018}, S. Tomforde, B. Sick (Hrsg.) (2019).
    • 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.
    • {SPEC Research Group News...
      {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)
    • Chamulteon: Coordinated A...
      Chamulteon: Coordinated Auto-Scaling of Micro-Services. A. Bauer; V. Lesch; L. Versluis; A. Ilyushkin; N. Herbst; S. Kounev; in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
    • {Systematic Search for Op...
      {Systematic Search for Optimal Resource Configurations of Distributed Applications}. A. Bauer; S. Eismann; J. Grohmann; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 120–125.
    • {Autonomic Forecasting Me...
      {Autonomic Forecasting Method Selection: Examination and Ways Ahead}. M. Z{\"u}fle; A. Bauer; V. Lesch; C. Krupitzer; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 16th {IEEE} International Conference on Autonomic Computing (ICAC) (2019).
    • {Utilizing Clustering to ...
      {Utilizing Clustering to Optimize Resource Demand Estimation Approaches}. J. Grohmann; S. Eismann; A. Bauer; M. Zuefle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134–139.
    • {TeaStore - A Micro-Servi...
      {TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research}. S. Eismann; (2019, Februar).
    • {Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge}. S. Kounev; (2019, Juni).
    • Hands Off my Database: Ra...
      Hands Off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences L. Iffländer; A. Dmitrienko; C. Hagen; M. Jobst; S. Kounev; (2019).
    • {From the Internet-of-Things to the Internet-of-Thinking-Things}. S. Kounev; (2019, Juli).
    • {Detecting Parametric Dep...
      {Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques}. J. Grohmann; S. Eismann; S. Elflein; M. Mazkatli; J. von Kistowski; S. Kounev; in 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2019). 309–322.
      {Acceptance Rate: 23.8% (29/122)}
    • 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).
    • 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).
    • {Predicting Server Power ...
      {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)}
    • {Modeling of Aggregated I...
      {Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud}. F. Metzger; T. Ho{\ss}feld; A. Bauer; S. Kounev; P. E. Heegaard; in Proceedings of the IEEE (2019). 107(4) 679–694.
    • {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).
    • Measuring, Rating, and Predicting the Energy Efficiency of Servers. J. von Kistowski; Thesis; University of W{\"u}rzburg, Germany. (2019, März).
    • {Methoden und Messverfahr...
      {Methoden und Messverfahren f{\"u}r 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).
    • 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.
    • {Integrating Statistical ...
      {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)
    • {On Learning Parametric D...
      {On Learning Parametric Dependencies from Monitoring Data}. J. Grohmann; S. Eismann; S. Kounev; in Proceedings of the 10th Symposium on Software Performance 2019 (SSP’19) (2019).
    • {Artificial Intelligence in Medicine - From Data Collection to Prediction}. M. Z{\"u}fle; R. Leppich; (2019, November).
    • {Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation}. F. Erhard; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, September).
    • Exploring the {L}imitations of {S}tatistical {R}esponse {T}ime {M}odels in {A}rchitectural {P}erformance {M}odels. S. Trapp; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, Dezember).
    • {An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics}. T. Dreher; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, Oktober).
    • {Erweiterung von Komponenten-basierten Performance Modellen f{\"u}r Micro-service Architekturen}. L. Hick; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, Februar).
    • {Best Practices for Time ...
      {Best Practices for Time Series Forecasting}. A. Bauer; M. Z{\"u}fle; N. Herbst; S. Kounev; in {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)} (2019).
    • Performance Oriented Dyna...
      Performance Oriented Dynamic Bypassing for Intrusion Detection Systems. L. Iffländer; J. Stoll; N. Rawtani; V. Lesch; K.-D. Lange; S. Kounev; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 159–166.
    • {Please Obey My Plan: How...
      {Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems}. V. Lesch; C. Krupitzer; S. Tomforde; (2019, April).
    • The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019. C. Krupitzer; S. Tomforde; in Proceedings of the INFORMATIK 2019 (Workshop-Beitr{\"a}ge) (2019).
    • {Chameleon: A Hybrid, Pro...
      {Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}. A. Bauer; (2019, Juni).
    • {Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads}. K. Tocze; N. Schmitt; I. Brandic; A. Aral; S. Nadjm-Tehrani; in Proceedings of Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf) as part of FAS*(IEEE ICAC/SASO) conferences companion (2019).
    • On Learning in Collective...
      On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. M. {D’Angelo}; S. {Gerasimou}; S. {Ghahremani}; J. {Grohmann}; I. {Nunes}; E. {Pournaras}; S. {Tomforde}; in Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (2019). 13–24.
    • {A Concept for an Adaptiv...
      {A Concept for an Adaptive Communication Middleware for Car-2-Cloud Applications}. S. Herrnleben; (2019, April).
    • {Multi-objective Optimisa...
      {Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}. V. Lesch; C. Krupitzer; S. Tomforde; in Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019 (2019).
    • {Emerging Self-Integratio...
      {Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}. V. Lesch; C. Krupitzer; S. Tomforde; in Proceedings of the 4th {IEEE} International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019 (2019).
    • Discrete-Time Analysis of the Blockchain Distributed Ledger Technology. S. Geissler; T. Prantl; S. Lange; F. Wamser; T. Hossfeld; in Proceedings of the 31st International Teletraffic Congress (ITC) (2019).