%0 Conference Paper %1 BaGrHeKo2018-MMB-ServiceDemand %A Bauer, André %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) %C Cham %D 2018 %I Springer %K Application-aware Application_quality_of_service_management Automated_model_learning BUNGEE Chameleon Cloud Elasticity GoogleResearchAward Instrumentation_profiling_and_workload_characterization LibReDE Metrics_and_benchmarking_methodologies PRISMA Prediction Resource_management Tool Virtualization descartes t_full myown %P 142--156 %R 10.1007/978-3-319-74947-1_10 %T On the Value of Service Demand Estimation for Auto-Scaling %U https://doi.org/10.1007/978-3-319-74947-1_10 %V 10740 %X In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. This is shown by testing two approaches in three different scenarios. Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. Our results encourage further research on the application of service demand estimates for resource management in data centers. %@ 978-3-319-74947-1

@inproceedings{BaGrHeKo2018-MMB-ServiceDemand, abstract = {In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. This is shown by testing two approaches in three different scenarios. Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. Our results encourage further research on the application of service demand estimates for resource management in data centers.}, added-at = {2020-04-06T11:24:29.000+0200}, address = {Cham}, author = {Bauer, Andr{\'e} and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/2cacde622abcb03220e93058caa985ad4/joh.grohmann}, booktitle = {Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018)}, doi = {10.1007/978-3-319-74947-1_10}, interhash = {f779636d67e3da35264023a970304f7c}, intrahash = {cacde622abcb03220e93058caa985ad4}, isbn = {978-3-319-74947-1}, keywords = {Application-aware Application_quality_of_service_management Automated_model_learning BUNGEE Chameleon Cloud Elasticity GoogleResearchAward Instrumentation_profiling_and_workload_characterization LibReDE Metrics_and_benchmarking_methodologies PRISMA Prediction Resource_management Tool Virtualization descartes t_full myown}, month = {February}, pages = {142--156}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2022-11-16T09:10:40.000+0100}, title = {{On the Value of Service Demand Estimation for Auto-Scaling}}, url = {https://doi.org/10.1007/978-3-319-74947-1_10}, volume = 10740, year = 2018 }