application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications

ISCAS OpenIR

	application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications
	Wang Wei; Huang Xiang; Qin Xiulei; Zhang Wenbo; Wei Jun; Zhong Hua
	2012
会议名称	2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
会议录名称	Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
页码	439-446
会议日期	June 24, 2012 - June 29, 2012
会议地点	Honolulu, HI, United states
收录类别	EI
ISBN	9780769547558
部门归属	(1) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing 100190 China
摘要	Performance isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading. © 2012 IEEE.; Performance isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading. © 2012 IEEE.
关键词	Cloud Computing Regression Analysis
主办者	IEEE; IEEE Computer Society; TC-SVC; IBM; SAP
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15797
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang Wei,Huang Xiang,Qin Xiulei,et al. application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications[C],2012:439-446.