title: A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.
abstract: The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China.
authors.forename: Yu-Shuang
authors.initials: YS
authors.lastname: Dong
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Gao-Chao
authors.initials: GC
authors.lastname: Xu
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China.
authors.forename: Xiao-Dong
authors.initials: XD
authors.lastname: Fu
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*