{"title":"云计算环境下基于粒子群算法的资源分配策略","authors":"Fu Xie, Y. Du, Hongwei Tian","doi":"10.1109/ICDMA.2013.18","DOIUrl":null,"url":null,"abstract":"Allocating the resources efficiently in cloud computing environment is not only an important issue and but also a research focus. A strategy of resource allocation and price adjustment based on particle swarm algorithm is proposed in this paper. According to the workload characteristics, a utility function is designed to evaluate QoS. According to the resource demand from all workloads, the resource prices are dynamically adjusted by the corresponding resource agents in order to obtain maximum profits for each workload. The results of the simulation experiment show that our strategy has effectiveness and robustness.","PeriodicalId":403312,"journal":{"name":"2013 Fourth International Conference on Digital Manufacturing & Automation","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Resource Allocation Strategy Based on Particle Swarm Algorithm in Cloud Computing Environment\",\"authors\":\"Fu Xie, Y. Du, Hongwei Tian\",\"doi\":\"10.1109/ICDMA.2013.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Allocating the resources efficiently in cloud computing environment is not only an important issue and but also a research focus. A strategy of resource allocation and price adjustment based on particle swarm algorithm is proposed in this paper. According to the workload characteristics, a utility function is designed to evaluate QoS. According to the resource demand from all workloads, the resource prices are dynamically adjusted by the corresponding resource agents in order to obtain maximum profits for each workload. The results of the simulation experiment show that our strategy has effectiveness and robustness.\",\"PeriodicalId\":403312,\"journal\":{\"name\":\"2013 Fourth International Conference on Digital Manufacturing & Automation\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth International Conference on Digital Manufacturing & Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMA.2013.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth International Conference on Digital Manufacturing & Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMA.2013.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Resource Allocation Strategy Based on Particle Swarm Algorithm in Cloud Computing Environment
Allocating the resources efficiently in cloud computing environment is not only an important issue and but also a research focus. A strategy of resource allocation and price adjustment based on particle swarm algorithm is proposed in this paper. According to the workload characteristics, a utility function is designed to evaluate QoS. According to the resource demand from all workloads, the resource prices are dynamically adjusted by the corresponding resource agents in order to obtain maximum profits for each workload. The results of the simulation experiment show that our strategy has effectiveness and robustness.