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| 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 | |
| Conference Name | 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 |
| Source | Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 |
| Pages | 439-446 |
| Conference Date | June 24, 2012 - June 29, 2012 |
| Conference Place | Honolulu, HI, United states |
| Indexed Type | EI |
| ISBN | 9780769547558 |
| Department | (1) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing 100190 China |
| 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.; 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. |
| Keyword | Cloud Computing Regression Analysis |
| Sponsorship | IEEE; IEEE Computer Society; TC-SVC; IBM; SAP |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15797 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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