{"title":"Nines are Not Enough: Meaningful Metrics for Clouds","authors":"J. Mogul, J. Wilkes","doi":"10.1145/3317550.3321432","DOIUrl":null,"url":null,"abstract":"Cloud customers want strong, understandable promises (Service Level Objectives, or SLOs) that their applications will run reliably and with adequate performance, but cloud providers don't want to offer them, because they are technically hard to meet in the face of arbitrary customer behavior and the hidden interactions brought about by statistical multiplexing of shared resources. Existing cloud SLOs are more concerned with defending against corner cases than defining normal behavior. This and other tensions make SLOs surprisingly hard to define. We show that this problem shares some similarities with the challenges of applying statistics to make decisions based on sampled data. We argue that a mutually beneficial set of Service Level Expectations (SLEs) and Customer Behavior Expectations (CBEs) ameliorates many of the problems of today's SLOs by explicitly sharing risk between customer and service provider.","PeriodicalId":224944,"journal":{"name":"Proceedings of the Workshop on Hot Topics in Operating Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Workshop on Hot Topics in Operating Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3317550.3321432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26
Abstract
Cloud customers want strong, understandable promises (Service Level Objectives, or SLOs) that their applications will run reliably and with adequate performance, but cloud providers don't want to offer them, because they are technically hard to meet in the face of arbitrary customer behavior and the hidden interactions brought about by statistical multiplexing of shared resources. Existing cloud SLOs are more concerned with defending against corner cases than defining normal behavior. This and other tensions make SLOs surprisingly hard to define. We show that this problem shares some similarities with the challenges of applying statistics to make decisions based on sampled data. We argue that a mutually beneficial set of Service Level Expectations (SLEs) and Customer Behavior Expectations (CBEs) ameliorates many of the problems of today's SLOs by explicitly sharing risk between customer and service provider.