A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.
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 sha...
| Published in: | Scientific World Journal pp. 259139 - 259140 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103838646&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103838646 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103838646 NLM25097872 2012672050 10.1155/2014/259139 NLM25097872 PMC4109368 103838646 ppf: 259139 ppct: 1 formats: tig: atl: A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform. aug: au: Dong, Yu-Shuang Xu, Gao-Chao Fu, Xiao-Dong affil: College of Computer Science and Technology, Jilin University, Changchun 130012, China. sug: subj: Algorithms Information Retrieval Methods ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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