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.

    2023[ to top ]
    • Recommendation of Secure Group Communication Schemes using Multi Objective Optimization T. Prantl; A. Bauer; L. Iffländer; C. Krupitzer; S. Kounev; in International Journal of Information Security (2023).
    • Power to the Applications...
      Power to the Applications: The Vision of Continuous Decentralized Autoscaling M. Straesser; S. Geißler; T. Hoßfeld; S. Kounev; in 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) (2023).
      In print.
    • Optimizing storage assign...
      Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses V. Lesch; P. B. Müller; M. Krämer; M. Hadry; S. Kounev; C. Krupitzer; in Applied intelligence (2023). 25.
    • A Systematic Approach for...
      A Systematic Approach for Benchmarking of Container Orchestration Frameworks M. Straesser; J. Mathiasch; A. Bauer; S. Kounev; in Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (2023).
      In print.
    • Autoscaler Evaluation and...
      Autoscaler Evaluation and Configuration: A Practitioner’s Guideline M. Straesser; S. Eismann; J. von Kistowski; A. Bauer; S. Kounev; in Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (2023).
      In print.
    • A literature review of Io...
      A literature review of IoT and CPS—What they are, and what they are not V. Lesch; M. Züfle; A. Bauer; L. Iffländer; C. Krupitzer; S. Kounev; in Journal of Systems and Software (2023). 111631.
    • Association between Postoperative Atrial Fibrillation Recurrences and Interatrial Block by the Time of Open-Heart Surgery S. Leiler; J. Kalisnik; R. Bernik; A. Bauer; V. Günzler; P. Sluet; S. Kounev; T. Fischlein; in The Thoracic and Cardiovascular Surgeon (2023). 71(S 01) DGTHG-V130.
    • Serverless Computing Revisited: Evolution, State-of-the-Art, and Performance Challenges S. Kounev; (2023).
    • An Empirical Study of Con...
      An Empirical Study of Container Image Configurations and Their Impact on Start Times M. Straesser; A. Bauer; R. Leppich; N. Herbst; K. Chard; I. Foster; S. Kounev; in 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (2023).
      In print.
    • MVNOCoreSim: A Digital Twin for Virtualized IoT-centric Mobile Core Networks S. Geissler; F. Wamser; W. Bauer; S. Gebert; S. Kounev; T. Hossfeld; in IEEE Internet of Things (2023).
    2022[ to top ]
    • 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).
    • ICPE ’22: ACM/SPEC International Conference on Performance Engineering, Bejing, China, April 9 - 13, 2022, Companion Volume D. Feng; S. Becker; N. Herbst; P. Leitner; (D. Feng; S. Becker; N. Herbst; P. Leitner, Hrsg.) (2022). ACM.
    • 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.
    • The Relationship Between the Distribution of Training Intensity and Performance of Kayak and Canoe Sprinters: A Retrospective Observational Analysis of One Season of Competition M. Matzka; R. Leppich; H.-C. Holmberg; B. Sperlich; C. Zinner; in Frontiers in Sports and Active Living (2022). 3 788108.
    • Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial P. Helmer; S. Hottenrott; P. Rodemers; R. Leppich; M. Helwich; R. Pryss; P. Kranke; P. Meybohm; B. E. Winkler; M. Sammeth; in Journal of Medical Internet Research (2022). 24(12) e42359.
    • 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 (2022). 48(10) 4152–4166.
    • Edge Workload Trace Gathering and Analysis for Benchmarking K. Toczé; N. Schmitt; U. Kargén; A. Aral; I. Brandi’c; in 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC) (2022). 34–41.
    • Investigating the Predictability of QoS Metrics in Cellular Networks S. Herrnleben; J. Grohmann; V. Lesch; T. Prantl; F. Metzger; T. Hoßfeld; S. Kounev; in 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) (2022). 1–10.
    • ICPE ’22: ACM/SPEC International Conference on Performance Engineering, Bejing, China, April 9 - 13, 2022 D. Feng; S. Becker; N. Herbst; P. Leitner; (D. Feng; S. Becker; N. Herbst; P. Leitner, Hrsg.) (2022). ACM.
    • 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.
    • Proactive Critical Event Prediction based on Monitoring Data with Focus on Technical Systems M. Züfle; Thesis; Universität Würzburg. (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).
    • Retrospektive Analyse ausgewählter Belastungs-und Beanspruchungsparameter einer professionellen Fußball-mannschaft vor und während coronabedingter Kontakt-beschränkungen in der Spielsaison 2019/20 P. Kunz; P. Düking; R. Leppich; J. Lachmann; B. Sperlich; in Leipziger Sportwissenschaftliche Beiträge Jahrgang 63 (2022 Heft 1 (2022). 75.
    • Identification of clinically distinct and prognostically important phenogroups in patients with acute heart failure by a machine learning approach J. J. Albert; R. Leppich; V. Borst; F. Sahiti; N. Scholz; V. Cejka; C. Morbach; G. Ertl; C. Angermann; S. Frantz; others; in EUROPEAN JOURNAL OF HEART FAILURE (2022). (Bd. 24) 179–179.
    • Retrospective Analysis of Training Intensity Distribution Based on Race Pace Versus Physiological Benchmarks in Highly Trained Sprint Kayakers M. Matzka; R. Leppich; B. Sperlich; C. Zinner; in Sports Medicine-Open (2022). 8(1) 1–12.
    • 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.
    • Self-Aware Optimization of Adaptation Planning Strategies V. Lesch; M. Hadry; S. Kounev; C. Krupitzer; in ACM Transactions on Autonomous and Adaptive Systems (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).
    • Self-Aware Optimization of Cyber-Physical Systems in Intelligent Transportation and Logistics Systems V. Lesch; Thesis; Universität Würzburg. (2022, Mai).
    • 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.
    • 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)
    • Model Learning for Perfor...
      Model Learning for Performance Prediction of Cloud-native Microservice Applications J. Grohmann; Thesis; Universität Würzburg. (2022, März).
      Distinction
    • 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
    • Measurement, Modeling, and Emulation of Power Consumption of Distributed Systems N. Schmitt; (2022, Juli).
    • 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).
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, Issue 11 S. Kounev; A. van Hoorn; M. Straesser; A. Bauer; (2022, Mai).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • 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.
    • 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).
    • 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).
    • 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).
    • 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).
    • 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).
    • Leveraging Kubernetes Source Code for Performance Simulation M. Straesser; P. Haas; (2022).
    2021[ to top ]
    • 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%
    • Attack-aware Security Fun...
      Attack-aware Security Function Management L. Iffländer; Thesis; Universität Würzburg. (2021, Januar).
    • The Science of Systems Be...
      The Science of Systems Benchmarking S. Kounev; (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
    • Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases A. Bauer; Thesis; Universität Würzburg. (2021).
    • A Case Study on Optimization of Warehouses V. Lesch; P. Müller; M. Krämer; S. Kounev; C. Krupitzer; in arXiv preprint arXiv:2112.12058 (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).
    • 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).
    • 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.
    • Automatisierte Hybride Zeitreihenprognose A. Bauer; in Ausgezeichnete Informatikdissertationen 2020, S. Hölldobler (Hrsg.) (2021). 29–38.
    • 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)
    • 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).
    • 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.
    • Container Start Times: Em...
      Container Start Times: Empirical Analysis and Predictability M. Straesser; (2021).
    • 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
    • A Case Study of Vehicle R...
      A Case Study of Vehicle Route Optimization V. Lesch; M. König; S. Kounev; A. Stein; C. Krupitzer; (2021).
    • 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 on Optimizat...
      A Case Study on Optimization of Warehouses V. Lesch; P. B. Müller; M. Krämer; S. Kounev; C. Krupitzer; (2021).
    • A New Course on Systems B...
      A New Course on Systems Benchmarking-For Scientists and Engineers S. Kounev; (2021). 127–127.
    • 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.
    • 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).
    • 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).
    • 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).
    • 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.
    • 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.
    • 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)
    • 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).
    • 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%
    • 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)
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, vol. 3 no. 2 S. Kounev; A. van Hoorn; A. Bauer; (2021, Mai).
      Published by Standard Performance Evaluation Corporation (SPEC)
    • 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
    • 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.
    • 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.
    • 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.
    • Systems Benchmarking S. Kounev; (2021).
    • 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.
    2020[ to top ]
    • 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.
    • Predicting Performance De...
      Predicting Performance Degradations of Black-box Microservice Applications M. Straesser; (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.
    • 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.
    • Systems Benchmarking S. Kounev; K.-D. Lange; J. von Kistowski; (2020). (1. Aufl.) Springer International Publishing.
    • 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.
    • 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.
    • 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.
    • 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).
    • 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).
    • 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).
    • 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).
    • 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).
    • 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).
    • 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).
    • 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%
    • 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.
    • 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%
    • 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).
    • 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.
    • 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.
    • 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 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).
    • 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)
    • SPEC Research Group Newsl...
      SPEC Research Group Newsletter, vol. 3 no. 1 S. Kounev; A. van Hoorn; A. Bauer; N. Herbst; (2020, Mai).
      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 Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, März).
    • 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.
    • 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).
    • 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.
    • 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).
    • 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)
    • 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).
    • 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
    • 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).
    • 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).
    • 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
    • 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.
    2019[ to top ]
    • 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.
    • 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)
    • Systematic Search for Opt...
      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 Met...
      Autonomic Forecasting Method Selection: Examination and Ways Ahead M. Züfle; A. Bauer; V. Lesch; C. Krupitzer; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
    • Online Power Consumption ...
      Online Power Consumption Estimation for Functions in Cloud Applications N. Schmitt; L. Iffländer; A. Bauer; S. Kounev; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
    • Utilizing Clustering to O...
      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.
    • Der Weg vom Internet-der-Dinge zum Internet-der-denkenden-Dinge S. Kounev; (2019, Juli).
    • From the Internet-of-Things to the Internet-of-Thinking-Things S. Kounev; (2019, Juli).
    • 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.
    • Detecting Parametric Depe...
      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).
    • On Learning Parametric De...
      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).