{"title":"Virtualized Resource Scheduling in Cloud Computing Environments: An Review","authors":"Jianpeng Lin, Delong Cui, Zhiping Peng, Qirui Li, Jieguang He, Mian Guo","doi":"10.1109/TOCS50858.2020.9339736","DOIUrl":null,"url":null,"abstract":"This paper summarizes the status and results of current research on cloud computing resource scheduling optimization problems. At the virtual resource layer, it focuses on analyzes and discussion pertaining to quality of service and efficiency-aware resource scheduling strategies, node task load balance and resource utilization-aware resource scheduling strategies, and multi-objective optimization resource scheduling strategies. At the physical resource layer, the paper focuses on data center load balance-aware resource scheduling strategies, energy consumption and cost-aware resource scheduling strategies, resource scheduling strategies with containers as virtual units, and reinforcement learning resource scheduling strategy. Finally, on the basis of the reviewed literature results, this paper proposes future research directions for cloud computing resource management and scheduling.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"360 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TOCS50858.2020.9339736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper summarizes the status and results of current research on cloud computing resource scheduling optimization problems. At the virtual resource layer, it focuses on analyzes and discussion pertaining to quality of service and efficiency-aware resource scheduling strategies, node task load balance and resource utilization-aware resource scheduling strategies, and multi-objective optimization resource scheduling strategies. At the physical resource layer, the paper focuses on data center load balance-aware resource scheduling strategies, energy consumption and cost-aware resource scheduling strategies, resource scheduling strategies with containers as virtual units, and reinforcement learning resource scheduling strategy. Finally, on the basis of the reviewed literature results, this paper proposes future research directions for cloud computing resource management and scheduling.