{"title":"RL-Based Scheduling Strategies in Actual Grid Environments","authors":"B. F. Costa, I. Dutra, M. Mattoso","doi":"10.1109/ISPA.2008.119","DOIUrl":null,"url":null,"abstract":"In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on reinforcement learning are a good choice to improve the overall performance of grid applications and resource utilization.","PeriodicalId":345341,"journal":{"name":"2008 IEEE International Symposium on Parallel and Distributed Processing with Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Parallel and Distributed Processing with Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPA.2008.119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on reinforcement learning are a good choice to improve the overall performance of grid applications and resource utilization.
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