On the Value of Service Demand Estimation for Auto-Scaling
A. Bauer, J. Grohmann, N. Herbst, and S. Kounev. Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018), volume 10740 of Lecture Notes in Computer Science, page 142--156. Cham, Springer, (February 2018)
DOI: 10.1007/978-3-319-74947-1_10
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.
%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 = {2020-04-06T11:24:30.000+0200},
address = {Cham},
author = {Bauer, Andr{\'e} and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/2cacde622abcb03220e93058caa985ad4/samuel.kounev},
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 = {2022-11-16T09:10:06.000+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
}