Publications
- [ Article ]
- [ Incollection ]
- [ Inproceedings ]
- [ Misc ]
- [ PhD thesis ]
- [ Proceedings ]
- [ Techreport ]
Article[ to top ]
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Serverless Computing: What It Is, and What It Is Not?. in Commun. ACM (2023). 66(9) 80–92.
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ExDe: Design space exploration of scheduler architectures and mechanisms for serverless data-processing. in Future Generation Computer Systems (2023).https://www.sciencedirect.com/science/article/pii/S0167739X23004211
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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)
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Serverless Applications:Why, When, and How?. in IEEE Software (2021). 38(1) 32–39.
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Serverless Computing (Dagstuhl Seminar 21201). in Dagstuhl Reports, (C. Abad; I. T. Foster; N. Herbst; A. Iosup, eds.) (2021). 11(4) 34–93.
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Time Series Forecasting for Self-Aware Systems. in Proceedings of the IEEE (2020). 108(7) 1068–1093.
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A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. in Computers (2020). 9(1) 10.
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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.
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Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. in IEEE Transactions on Software Engineering (2019). 47(8) 1528–1543.
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Agile Scalability Engineering: The ScrumScale Method. in IEEE Software (2019). 37(5) 77–85.to appear
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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.
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Quantifying Cloud Performance and Dependability: Taxonomy, Metric Design, and Emerging Challenges. in ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS) (2018). 3(4) 19:1–19:36.
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An Experimental Performance Evaluation of Autoscalers for Complex Workflows. in ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS) (2018). 3(2) 8:1–8:32.
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Modeling and Extracting Load Intensity Profiles. in ACM Transactions on Autonomous and Adaptive Systems (TAAS) (2017). 11(4) 23:1–23:28.
Incollection[ to top ]
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Metrics and Benchmarks for Self-Aware Computing Systems. in Self-Aware Computing Systems, S. Kounev, J. O. Kephart, A. Milenkoski, X. Zhu (eds.) (2017).
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Online Workload Forecasting. in Self-Aware Computing Systems, S. Kounev, J. O. Kephart, X. Zhu, A. Milenkoski (eds.) (2017).
Inproceedings[ to top ]
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An Empirical Study of Container Image Configurations and Their Impact on Start Times. in 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (2023). 94–105.
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A Trace-driven Performance Evaluation of Hash-based Task Placement Algorithms for Cache-enabled Serverless Computing. in 2023 ACM International Conference on Computing Frontiers (CF ’23) (2023).
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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).
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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).Acceptance Rate: 29%
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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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Predicting the Costs of Serverless Workflows. in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020). 265–276.Acceptance Rate: 23.4% (15/64)
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Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
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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.
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Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. in Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria, M. Felderer, W. Hasselbring, R. Rabiser, R. Jung (eds.) (2020). (Vol. P-300) 93–94.
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Elasticity of Cloud Platforms. in Systems Benchmarking: For Scientists and Engineers, S. Kounev, K.-D. Lange, J. von Kistowski (eds.) (2020). 319–340.
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Kaa: Evaluating Elasticity of Cloud-hosted DBMS. in Proceedings of the 11th IEEE International Conference on Cloud Computing (CloudCom) (2019).
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Chamulteon: Coordinated Auto-Scaling of Micro-Services. in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
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Autonomic Forecasting Method Selection: Examination and Ways Ahead. in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
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Best Practices for Time Series Forecasting. in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (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.
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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.
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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.
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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.
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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.
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FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds. in Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018) (2018).Full Paper Acceptance Ratio: 23,7%
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Telescope: A Hybrid Forecast Method for Univariate Time Series. in Proceedings of the International work-conference on Time Series (ITISE 2017) (2017).
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Scalability Analysis of Cloud Software Services. in Companion Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017), Self Organizing Self Managing Clouds Workshop (SOSeMC 2017) (2017).
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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.
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Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
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An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).Best Paper Candidate (1/4)
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Proactive Memory Scaling of Virtualized Applications. in VMware R&D Innovation Offsite (RADIO) (2015).
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Proactive Memory Scaling of Virtualized Applications. in Proceedings of the 2015 IEEE 8th International Conference on Cloud Computing (IEEE CLOUD 2015) (2015). 277–284.Acceptance Rate: 15%
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Modeling and Extracting Load Intensity Profiles. in Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015) (2015).Acceptance rate: 29%
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Using and Extending LIMBO for the Descriptive Modeling of Arrival Behaviors. in Proceedings of the Symposium on Software Performance 2014 (2014). 131–140.Best Poster Award
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LIMBO: A Tool For Modeling Variable Load Intensities. in Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014). 225–226.
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Modeling Variations in Load Intensity over Time. in Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014). 1–4.
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Optimization Method for Request Admission Control to Guarantee Performance Isolation. in Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability (HotTopiCS 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014).
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Elasticity in Cloud Computing: What it is, and What it is Not. in Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013) (2013).Acceptance Rate (Short Paper): 36.9%
Misc[ to top ]
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Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field. (2023).
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SPEC Research Group Newsletter, vol. 3 no. 1. (2020, April).Published by Standard Performance Evaluation Corporation (SPEC)
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Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. (2019, June).
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SPEC Research Group Newsletter, vol. 2 no. 4. (2019, April).Published by Standard Performance Evaluation Corporation (SPEC)
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How we built a scalable micro-service application - The Tea Store. (2018, July).
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SPEC Research Group Newsletter, vol. 2 no. 3. (2018, April).Published by Standard Performance Evaluation Corporation (SPEC)
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SPEC Research Group Newsletter, vol. 2 no. 2. (2017, April).Published by Standard Performance Evaluation Corporation (SPEC)
PhD thesis[ to top ]
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Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments. Thesis; University of Würzburg, Germany. (2018, July).Distinction, SPEC Kaivalya Dixit Distinguished Dissertation Award 2018
Proceedings[ to top ]
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ICPE ’22: ACM/SPEC International Conference on Performance Engineering, Bejing, China, April 9 - 13, 2022 (D. Feng; S. Becker; N. Herbst; P. Leitner, eds.) (2022). ACM.
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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, eds.) (2022). ACM.
Techreport[ to top ]
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A Review of Serverless Use Cases and their Characteristics (2020).
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Methodological Principles for Reproducible Performance Evaluation in Cloud Computing - A SPEC Research Technical Report. (SPEC-RG-2019-04), (2019).
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Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics. (SPEC-RG-2016-01), (2016).
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Performance-oriented DevOps: A Research Agenda. (SPEC-RG-2015-01), (2015).