a profit-aware virtual machine deployment optimization framework for cloud platform providers

ISCAS OpenIR

	a profit-aware virtual machine deployment optimization framework for cloud platform providers
	Chen Wei; Qiao Xiaoqiang; Wei Jun; Huang Tao
	2012
会议名称	2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
会议录名称	Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
页码	17-24
会议日期	June 24, 2012 - June 29, 2012
会议地点	Honolulu, HI, United states
收录类别	EI
ISBN	9780769547558
部门归属	(1) Institute of Software Chinese Academy of Sciences Beijing China
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.; 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.
关键词	Cloud Computing Computer Simulation Optimization Profitability
主办者	IEEE; IEEE Computer Society; TC-SVC; IBM; SAP
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15787
专题	中国科学院软件研究所
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],2012:17-24.