application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications
文献类型：会议论文
| 作者 | Wang Wei ; Huang Xiang ; Qin Xiulei ; Zhang Wenbo ; Wei Jun ; Zhong Hua |
| 出版日期 | 2012 |
| 会议名称 | 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 |
| 会议日期 | June 24, 2012 - June 29, 2012 |
| 会议地点 | Honolulu, HI, United states |
| 关键词 | Cloud computing Regression analysis |
| 页码 | 439-446 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | IEEE; IEEE Computer Society; TC-SVC; IBM; SAP |
| 会议录 | Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
 |
| 语种 | 英语 |
| ISBN号 | 9780769547558 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15797]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012. Honolulu, HI, United states. June 24, 2012 - June 29, 2012. |
入库方式： OAI收割
来源：软件研究所
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