{"title":"基于改进粒子群算法的云计算虚拟机部署策略","authors":"Shanchen Pang, Dong Dekun, Shuyu Wang","doi":"10.31577/cai_2020_1-2_83","DOIUrl":null,"url":null,"abstract":"Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2020-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing\",\"authors\":\"Shanchen Pang, Dong Dekun, Shuyu Wang\",\"doi\":\"10.31577/cai_2020_1-2_83\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.\",\"PeriodicalId\":55215,\"journal\":{\"name\":\"Computing and Informatics\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2020-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computing and Informatics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.31577/cai_2020_1-2_83\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing and Informatics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.31577/cai_2020_1-2_83","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing
Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.
期刊介绍:
Main Journal Topics:
COMPUTER ARCHITECTURES AND NETWORKING
PARALLEL AND DISTRIBUTED COMPUTING
THEORETICAL FOUNDATIONS
SOFTWARE ENGINEERING
KNOWLEDGE AND INFORMATION ENGINEERING
Apart from the main topics given above, the Editorial Board welcomes papers from other areas of computing and informatics.