{"title":"基于虚拟机分组的任务调度提升云计算性能","authors":"Negar Chitgar, H. Jazayeriy, M. Rabiei","doi":"10.1109/IranianCEE.2019.8786391","DOIUrl":null,"url":null,"abstract":"The incredible rise of virtualization technology in cloud environments results the fostering workload which needs services provided by cloud resources. Task scheduling and Load balancing amongst the VMs and minimizing the makespan of the tasks are stimulating research concerns. In this paper, a method was introduced for scheduling workload based on VM grouping in cloud environments. The aim of the proposed method is improving cloud computing performance by reducing makespan and response time, and also through increasing VMs utilization. We evaluated the proposed algorithm with existing methods using various performance metrics. Evaluation results show that our proposed algorithm outperforms similar methods.","PeriodicalId":6683,"journal":{"name":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","volume":"54 1","pages":"2095-2099"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Improving Cloud Computing Performance Using Task Scheduling Method Based on VMs Grouping\",\"authors\":\"Negar Chitgar, H. Jazayeriy, M. Rabiei\",\"doi\":\"10.1109/IranianCEE.2019.8786391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The incredible rise of virtualization technology in cloud environments results the fostering workload which needs services provided by cloud resources. Task scheduling and Load balancing amongst the VMs and minimizing the makespan of the tasks are stimulating research concerns. In this paper, a method was introduced for scheduling workload based on VM grouping in cloud environments. The aim of the proposed method is improving cloud computing performance by reducing makespan and response time, and also through increasing VMs utilization. We evaluated the proposed algorithm with existing methods using various performance metrics. Evaluation results show that our proposed algorithm outperforms similar methods.\",\"PeriodicalId\":6683,\"journal\":{\"name\":\"2019 27th Iranian Conference on Electrical Engineering (ICEE)\",\"volume\":\"54 1\",\"pages\":\"2095-2099\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 27th Iranian Conference on Electrical Engineering (ICEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IranianCEE.2019.8786391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IranianCEE.2019.8786391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving Cloud Computing Performance Using Task Scheduling Method Based on VMs Grouping
The incredible rise of virtualization technology in cloud environments results the fostering workload which needs services provided by cloud resources. Task scheduling and Load balancing amongst the VMs and minimizing the makespan of the tasks are stimulating research concerns. In this paper, a method was introduced for scheduling workload based on VM grouping in cloud environments. The aim of the proposed method is improving cloud computing performance by reducing makespan and response time, and also through increasing VMs utilization. We evaluated the proposed algorithm with existing methods using various performance metrics. Evaluation results show that our proposed algorithm outperforms similar methods.