Publications
Journal and Magazine Articles
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The State of Serverless Applications: Collection, Characterization, and Community Consensus. in Transactions on Software Engineering (2022). 48(10) 4152–4166.
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SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation. in ACM Transactions on Autonomous and Adaptive Systems (2021). 15(2) Association for Computing Machinery, New York, NY, USA.
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Serverless Applications:Why, When, and How?. in IEEE Software (2021). 38(1) 32–39.
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A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. in Computers (2020). 9(1) 10. Multidisciplinary Digital Publishing Institute (MDPI).
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The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms. in IEEE Internet Computing (2019). 23(6) 7–18. IEEE.
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Online model learning for self-aware computing infrastructures. in Journal of Systems and Software (2019). 147 1–16.
Full Conference Papers
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Why Is It Not Solved Yet? Challenges for Production-Ready Autoscaling. in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022). 105–115. Association for Computing Machinery, New York, NY, USA.
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{Investigating the Predictability of QoS Metrics in Cellular Networks}. in 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) (2022). 1–10.
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A Simulation-based Optimization Framework for Online Adaptation of Networks. in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools), H. Song, D. Jiang (eds.) (2021). 513–532. Springer International Publishing, Cham.
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A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0. in Proceedings of the 21st IEEE IES International Conference on Industrial Informatics (2021). IEEE.
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SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications. in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021). ACM, New York, NY, USA.Acceptance Rate: 29%
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Libra: A Benchmark for Time Series Forecasting Methods. in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021). ACM, New York, NY, USA.
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Sizeless: Predicting the Optimal Size of Serverless Functions. in Proceedings of the 22nd International MIDDLEWARE Conference (2021). 248–259.Best Student Paper Award, ACM Artifacts Evaluated — Functional
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ComBench: A Benchmarking Framework for Publish/Subscribe Communication Protocols Under Network Limitations. in Performance Evaluation Methodologies and Tools, Q. Zhao, L. Xia (eds.) (2021). 72–92. Springer International Publishing, Cham.
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Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS. in 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020). 1–8. IEEE.Acceptance Rate: 27%
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An IoT Network Emulator for Analyzing the Influence of Varying Network Quality. in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) (2020).
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Predicting the Costs of Serverless Workflows. in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020). 265–276. Association for Computing Machinery (ACM), New York, NY, USA.{Acceptance Rate: 23.4% (15/64)}
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Incremental Calibration of Architectural Performance Models with Parametric Dependencies. in 2020 IEEE International Conference on Software Architecture (ICSA 2020) (2020). 23–34. IEEE.
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{To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows}. in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020). 19–36. Springer, Cham.
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{Model-based Performance Predictions for SDN-based Networks: A Case Study}. in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020). Springer, Cham.
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{Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques}. in 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2019). 309–322. IEEE Computer Society.{Acceptance Rate: 23.8% (29/122)}
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On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. in Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (2019). 13–24. IEEE Press.
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{Integrating Statistical Response Time Models in Architectural Performance Models}. in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80. IEEE.Acceptance Rate: 21,9\% (21/96)
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Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning. in Proceedings of the 20th International Middleware Conference (2019). 149–162. Association for Computing Machinery (ACM), New York, NY, USA.
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{Predicting Server Power Consumption from Standard Rating Results}. in Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering (2019). 301–312. Association for Computing Machinery (ACM), New York, NY, USA.{Full Paper Acceptance Rate: 18.6\% (13/70)}
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{TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research}. in Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (2018). 223–236. IEEE Computer Society.{Acceptance Rate: 29.5\% (23/78)}
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{On the Value of Service Demand Estimation for Auto-Scaling}. in Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) (2018). (Vol. 10740) 142–156. Springer, Cham.
Short Conference Papers
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Same, Same, but Dissimilar: Exploring Measurements for Workload Time-Series Similarity. in Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering (2022). 89–96. Association for Computing Machinery, New York, NY, USA.
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{An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series}. in Proceedings of the ACM/SPEC International Conference on Performance Engineering (2020). 48–55. Association for Computing Machinery (ACM), New York, NY, USA.
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{How is Performance Addressed in DevOps?}. in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 45–50. Association for Computing Machinery (ACM), New York, NY, USA.
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{Self-Tuning Resource Demand Estimation}. in Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017) (2017). 21–26.
Workshop Papers
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Optimizing Parametric Dependencies for Incremental Performance Model Extraction. in Companion of the 14th European Conference Software Architecture ({ECSA} 2020), H. Muccini, P. Avgeriou, B. Buhnova, J. C{{á}}mara, M. Caporuscio, M. Franzago, A. Koziolek, P. Scandurra, C. Trubiani, D. Weyns, U. Zdun (eds.) (2020). (Vol. 1269) 228–240. Springer, Cham.
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Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning. in 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (2020). 121–126. IEEE.
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{On Learning Parametric Dependencies from Monitoring Data}. in Proceedings of the 10th Symposium on Software Performance 2019 (SSP’19) (2019).
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{Systematic Search for Optimal Resource Configurations of Distributed Applications}. in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 120–125. IEEE Computer Society, Los Alamitos, CA, USA.
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{Utilizing Clustering to Optimize Resource Demand Estimation Approaches}. in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134–139. IEEE.
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Black-box Learning of Parametric Dependencies for Performance Models. in Proceedings of 13th International Workshop on Models@run.time (MRT), co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018) (2018).
Vision, Position, Demo, and Poster Papers
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Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads. in Companion of the ACM/SPEC International Conference on Performance Engineering (2021). (Vol. ICPE ’21) 175–178. Association for Computing Machinery, New York, NY, USA.
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TeaStore: A Micro-Service Reference Application for Cloud Researchers. in Proceedings of 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion) (2018). 11–12. IEEE.
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{The Vision of Self-Aware Performance Models}. in 2018 IEEE International Conference on Software Architecture Companion (ICSA-C) (2018). 60–63.
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Using Machine Learning for Recommending Service Demand Estimation Approaches. in Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018) (2018). 473–480. SciTePress.
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{Tools for Declarative Performance Engineering}. in Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 53–56. Association for Computing Machinery (ACM), New York, NY, USA.
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A {SPEC RG} Cloud Group’s Vision on the Performance Challenges of FaaS Cloud Architectures. in Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 21–24. Association for Computing Machinery (ACM), New York, NY, USA.