%0 Conference Paper %1 HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation %A Herrnleben, Stefan %A Grohmann, Johannes %A Rygielski, Pitor %A Lesch, Veronika %A Krupitzer, Christian %A Kounev, Samuel %B Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) %C Cham %D 2021 %E Song, Houbing %E Jiang, Dingde %I Springer International Publishing %K descartes myown networking t_full %P 513--532 %R 10.1007/978-3-030-72792-5_41 %T A Simulation-based Optimization Framework for Online Adaptation of Networks %U https://doi.org/10.1007/978-3-030-72792-5_41 %X Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations. %@ 978-3-030-72792-5

@inproceedings{HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation, abstract = {Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.}, added-at = {2020-09-04T11:46:07.000+0200}, address = {Cham}, author = {Herrnleben, Stefan and Grohmann, Johannes and Rygielski, Pitor and Lesch, Veronika and Krupitzer, Christian and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/240e96d2cb4b08fc78509ca0731226c64/samuel.kounev}, booktitle = {Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools)}, doi = {10.1007/978-3-030-72792-5_41}, editor = {Song, Houbing and Jiang, Dingde}, interhash = {7770d92eab2b2ed34901d46ed0277eb7}, intrahash = {40e96d2cb4b08fc78509ca0731226c64}, isbn = {978-3-030-72792-5}, keywords = {descartes myown networking t_full}, month = {August}, pages = {513--532}, publisher = {Springer International Publishing}, series = {SIMUtools 2020}, timestamp = {2022-11-16T09:09:51.000+0100}, title = {A Simulation-based Optimization Framework for Online Adaptation of Networks}, url = {https://doi.org/10.1007/978-3-030-72792-5_41}, year = 2021 }