Dynamically Estimating Approach for CPU Consumption of Multi-Tenancy Web Applications
Wei Wang, Xiang Huang, Wenbo Zhang, Jun Fang Wei, Hua Zhong, Tao Huang · Chinese Journal of Computers · 2012
Middleware sharing is one of the important resource sharing approaches in cloud computing.However,a shared middleware server easily causes interference in performance between multiple hosted tenants.This interference affects infrastructure resources as well as applications and services that are hosted on shared resources but that need to be made available in multiple performance isolated instances.A key requirement in performance isolation of the shared Java middleware server is the knowledge of the resource consumption of the various tenants.However,direct measurement of CPU resource consumption requires instrumentation,incurs overhead,and assumes OS support.Recently,regression analysis has been applied to indirectly approximate resource consumption,but challenges still remain in estimating time-varying states in dynamic systems.In this paper,we propose a Kalman filter-based approach to offer a solution to the problem of dynamically estimating the CPU consumption of a multi-tenancy Web application in a shared Java middleware server,and we discuss the challenges involved in this approach.We investigate factors that impact the efficiency and accuracy of the approach in estimating time-varying states via two case studies.Experimental results show that,even under continuously changing workload conditions,estimation results are in agreement with the corresponding measurements with acceptable estimation errors,especially with appropriately tuned filter settings taken into account.Our experiments also demonstrate the utility of our approach in identifying the aggressive tenants and in avoiding shared middleware server CPU overloading.