{"title":"面向云中经济和绿色MapReduce计算的最佳资源配置","authors":"Keke Chen, Shumin Guo, James Powers, F. Tian","doi":"10.1201/b17112-18","DOIUrl":null,"url":null,"abstract":"Running MapReduce programs in the cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge or job finish time for a specific job? An important step towards this ultimate goal is modeling the cost of MapReduce program. In this chapter, we study the whole process of MapReduce processing and build 1","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Toward Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud\",\"authors\":\"Keke Chen, Shumin Guo, James Powers, F. Tian\",\"doi\":\"10.1201/b17112-18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Running MapReduce programs in the cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge or job finish time for a specific job? An important step towards this ultimate goal is modeling the cost of MapReduce program. In this chapter, we study the whole process of MapReduce processing and build 1\",\"PeriodicalId\":448182,\"journal\":{\"name\":\"Large Scale and Big Data\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Large Scale and Big Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/b17112-18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Large Scale and Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/b17112-18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Toward Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud
Running MapReduce programs in the cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge or job finish time for a specific job? An important step towards this ultimate goal is modeling the cost of MapReduce program. In this chapter, we study the whole process of MapReduce processing and build 1