{"title":"Resources Allocation Method on Cloud Computing","authors":"Wei Ming, Chunyan Zhang, Qiu Feng, C. Yu, Qiangqiang Sui, Wanbing Ding","doi":"10.1109/ICSS.2014.50","DOIUrl":null,"url":null,"abstract":"To improve the efficiency of resource allocation cloud computing, as while as to improve resource utilization for the service provider, the paper has raised a polymorphic algorithm of Ant Colony Optimization, which can assure the quality of cloud service, and also dynamically change the list that contain nodes information. When the user submits the task, the algorithm will transfer it to the Cloud Control Queen by Master. And then, according to the functions, the ant colony will be divided into test ant colony, reconnaissance ant colony, cleared ant colony and workers ant colony. The algorithm can achieve the minimum average completion time gradually, and may reduce local optima, by forecasting the completion time and other pheromone.","PeriodicalId":206490,"journal":{"name":"2014 International Conference on Service Sciences","volume":"228 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Service Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSS.2014.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
To improve the efficiency of resource allocation cloud computing, as while as to improve resource utilization for the service provider, the paper has raised a polymorphic algorithm of Ant Colony Optimization, which can assure the quality of cloud service, and also dynamically change the list that contain nodes information. When the user submits the task, the algorithm will transfer it to the Cloud Control Queen by Master. And then, according to the functions, the ant colony will be divided into test ant colony, reconnaissance ant colony, cleared ant colony and workers ant colony. The algorithm can achieve the minimum average completion time gradually, and may reduce local optima, by forecasting the completion time and other pheromone.
为了提高云计算的资源分配效率,同时提高服务提供商的资源利用率,本文提出了一种蚁群优化的多态算法,既能保证云服务的质量,又能动态改变包含节点信息的列表。当用户提交任务时,算法会由Master将其传送到Cloud Control Queen。然后,根据蚁群的功能将其分为测试蚁群、侦察蚁群、清除蚁群和工蚁群。该算法通过预测完成时间和其他信息素,可逐步达到最小平均完成时间,并可减少局部最优值。