Predicting performance interference of application in virtualized environments

Yu Dai; Lei Yang; Hexu Xing; Bin Zhang

doi:10.1117/12.2051715

24 December 2013 Predicting performance interference of application in virtualized environments

Yu Dai, Lei Yang, Hexu Xing, Bin Zhang

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672B (2013) https://doi.org/10.1117/12.2051715
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

This paper proposes a method for predicting the performance interference of applications in the virtualized environment. In this method, we firstly analyze the relationship between the performance interference degree and the system-level workloads, and based on this we propose a linear regression algorithm to model relationship between the performance interference degree and the system-level workloads by using the historical data about performance interference degree as the training data set. For the applications without historical data about performance interference degree, we develop a method for predicting the performance interference by clustering the available models of performance interference and matchmaking between the workload pattern of the application and the workload patterns of the available models to generate the performance interference model for the application whose performance interference will to be predicted. By use of the available model, the performance interference of the application can be predicted without historical data about the performance interference among the applications co-located on the same physical host. The experiments show the effectiveness of the proposed measurement and prediction methods of the performance interference among the virtual machines.

Citation Download Citation

Yu Dai, Lei Yang, Hexu Xing, and Bin Zhang "Predicting performance interference of application in virtualized environments", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672B (24 December 2013); https://doi.org/10.1117/12.2051715

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