a profit-aware virtual machine deployment optimization framework for cloud platform providers
文献类型：会议论文
| 作者 | Chen Wei ; Qiao Xiaoqiang ; Wei Jun ; Huang Tao |
| 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 Computer simulation Optimization Profitability |
| 页码 | 17-24 |
| 中文摘要 | As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration. © 2012 IEEE. |
| 英文摘要 | As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration. © 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/311060/15787]  |
| 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 | Chen Wei,Qiao Xiaoqiang,Wei Jun,et al. a profit-aware virtual machine deployment optimization framework for cloud platform providers[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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