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| Performance modeling on the basis of application type in virtualized environments | |
| Meng, Fanxin (1); Du, Guangyu (2); He, Hong (2); Yuan, Shengzhong (2); He, H.(hehong@sdu.edu.cn) | |
| 2013 | |
| Source | Journal of Software
 |
| ISSN | 1796217X |
| Volume | 8Issue:11Pages:2847-2854 |
| English Abstract | Virtualization technology plays an essential role in resource in modern large data centers while it also causes interference among virtual machines which co-located in common physical machine contending for the shared physical resources. In this paper, we study the performance prediction models in virtualized environment. Unclassified model developed from all types of applications is quite inaccuracy to predict performance of test applications as it is too general. We respectively develop models for each type of applications classified by the resources that they use. See5/C5 technology is used to determine the type of test application before executing its corresponding performance prediction model and linear regression technique is adopt to develop performance prediction models. Finally, comparing classified models with the unclassified one, the former get 0.038 average prediction errors for test applications while unclassified model arrives 0.609 average prediction errors. © 2013 ACADEMY PUBLISHER.; Virtualization technology plays an essential role in resource in modern large data centers while it also causes interference among virtual machines which co-located in common physical machine contending for the shared physical resources. In this paper, we study the performance prediction models in virtualized environment. Unclassified model developed from all types of applications is quite inaccuracy to predict performance of test applications as it is too general. We respectively develop models for each type of applications classified by the resources that they use. See5/C5 technology is used to determine the type of test application before executing its corresponding performance prediction model and linear regression technique is adopt to develop performance prediction models. Finally, comparing classified models with the unclassified one, the former get 0.038 average prediction errors for test applications while unclassified model arrives 0.609 average prediction errors. © 2013 ACADEMY PUBLISHER. |
| Indexed Type | EI |
| Department | (1) School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Weihai, 264209, China; (2) Institute of Software of Chinese Academy of Sciences, Beijing. 100000, China |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17047 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | He, H.(hehong@sdu.edu.cn) |
| Recommended Citation GB/T 7714 | Meng, Fanxin ,Du, Guangyu ,He, Hong ,et al. Performance modeling on the basis of application type in virtualized environments[J]. Journal of Software,2013,8(11):2847-2854. |
| APA | Meng, Fanxin ,Du, Guangyu ,He, Hong ,Yuan, Shengzhong ,&He, H..(2013).Performance modeling on the basis of application type in virtualized environments.Journal of Software,8(11),2847-2854. |
| MLA | Meng, Fanxin ,et al."Performance modeling on the basis of application type in virtualized environments".Journal of Software 8.11(2013):2847-2854. |
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