{"title":"利用深度学习提高云微服务的性能可预测性","authors":"Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, Christina Delimitrou","doi":"10.1145/3352020.3352026","DOIUrl":null,"url":null,"abstract":"Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting UOS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"53 1","pages":"34 - 39"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3352020.3352026","citationCount":"13","resultStr":"{\"title\":\"Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer\",\"authors\":\"Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, Christina Delimitrou\",\"doi\":\"10.1145/3352020.3352026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting UOS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.\",\"PeriodicalId\":38935,\"journal\":{\"name\":\"Operating Systems Review (ACM)\",\"volume\":\"53 1\",\"pages\":\"34 - 39\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1145/3352020.3352026\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Operating Systems Review (ACM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3352020.3352026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operating Systems Review (ACM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3352020.3352026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer
Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting UOS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.
期刊介绍：
Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.
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