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Title:
application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications
Author: Wang Wei ; Huang Xiang ; Qin Xiulei ; Zhang Wenbo ; Wei Jun ; Zhong Hua
Source: Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Conference Name: 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Conference Date: June 24, 2012 - June 29, 2012
Issued Date: 2012
Conference Place: Honolulu, HI, United states
Keyword: Cloud computing ; Regression analysis
Indexed Type: EI
ISBN: 9780769547558
Department: (1) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing 100190 China
Sponsorship: IEEE; IEEE Computer Society; TC-SVC; IBM; SAP
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15797
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Wang Wei,Huang Xiang,Qin Xiulei,et al. application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications[C]. 见:2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012. Honolulu, HI, United states. June 24, 2012 - June 29, 2012.
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