{"title":"大规模生产环境下云块存储工作负载的深度分析","authors":"Jinhong Li, Qiuping Wang, P. Lee, Chao Shi","doi":"10.1109/IISWC50251.2020.00013","DOIUrl":null,"url":null,"abstract":"Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.","PeriodicalId":365983,"journal":{"name":"2020 IEEE International Symposium on Workload Characterization (IISWC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production\",\"authors\":\"Jinhong Li, Qiuping Wang, P. Lee, Chao Shi\",\"doi\":\"10.1109/IISWC50251.2020.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.\",\"PeriodicalId\":365983,\"journal\":{\"name\":\"2020 IEEE International Symposium on Workload Characterization (IISWC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Workload Characterization (IISWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IISWC50251.2020.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Workload Characterization (IISWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISWC50251.2020.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production
Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.
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