{"title":"一种高效节能的云数据中心虚拟机布局算法","authors":"Dan Liu, Xin Sui, Li Li","doi":"10.1109/FSKD.2016.7603263","DOIUrl":null,"url":null,"abstract":"The article puts forward an algorithm that combines Genetic Algorithm (GA) with Simulated Annealing (SA) which lower the energy consumption of the cloud data center. The energy-efficient virtual machine placement algorithm primarily accomplishes the population initialization according to the virtual machine placement rule and crossover, variation and correction. Finally, filter the new population on the basis of the metropolis rule. Experimental results show that the energy-efficient algorithm combines the advantages of GA and SA which have been improved a lot on global optimal solutions.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"An energy-efficient virtual machine placement algorithm in cloud data center\",\"authors\":\"Dan Liu, Xin Sui, Li Li\",\"doi\":\"10.1109/FSKD.2016.7603263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article puts forward an algorithm that combines Genetic Algorithm (GA) with Simulated Annealing (SA) which lower the energy consumption of the cloud data center. The energy-efficient virtual machine placement algorithm primarily accomplishes the population initialization according to the virtual machine placement rule and crossover, variation and correction. Finally, filter the new population on the basis of the metropolis rule. Experimental results show that the energy-efficient algorithm combines the advantages of GA and SA which have been improved a lot on global optimal solutions.\",\"PeriodicalId\":373155,\"journal\":{\"name\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2016.7603263\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603263","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An energy-efficient virtual machine placement algorithm in cloud data center
The article puts forward an algorithm that combines Genetic Algorithm (GA) with Simulated Annealing (SA) which lower the energy consumption of the cloud data center. The energy-efficient virtual machine placement algorithm primarily accomplishes the population initialization according to the virtual machine placement rule and crossover, variation and correction. Finally, filter the new population on the basis of the metropolis rule. Experimental results show that the energy-efficient algorithm combines the advantages of GA and SA which have been improved a lot on global optimal solutions.