{"title":"Benchmarking for Observability: The Case of Diagnosing Storage Failures","authors":"Duo Zhang, Mai Zheng","doi":"10.1016/j.tbench.2021.100006","DOIUrl":null,"url":null,"abstract":"<div><p>Diagnosing storage system failures is challenging even for professionals. One recent example is the “When Solid State Drives Are Not That Solid” incident occurred at Algolia data center, where Samsung SSDs were mistakenly blamed for failures caused by a Linux kernel bug. With the system complexity keeps increasing, diagnosing failures will likely become more difficult.</p><p>To better understand real-world failures and the potential limitations of state-of-the-art tools, we first conduct an empirical study on 277 user-reported storage failures in this paper. We characterize the issues along multiple dimensions (e.g., time to resolve, kernel components involved), which provides a quantitative measurement of the challenge in practice. Moreover, we analyze a set of the storage issues in depth and derive a benchmark suite called <span><math><mrow><mi>B</mi><mi>u</mi><mi>g</mi><mi>B</mi><mi>e</mi><mi>n</mi><mi>c</mi><msup><mrow><mi>h</mi></mrow><mrow><mi>k</mi></mrow></msup></mrow></math></span>. The benchmark suite includes the necessary workloads and software environments to reproduce 9 storage failures, covers 4 different file systems and the block I/O layer of the storage stack, and enables realistic evaluation of diverse kernel-level tools for debugging.</p><p>To demonstrate the usage, we apply <span><math><mrow><mi>B</mi><mi>u</mi><mi>g</mi><mi>B</mi><mi>e</mi><mi>n</mi><mi>c</mi><msup><mrow><mi>h</mi></mrow><mrow><mi>k</mi></mrow></msup></mrow></math></span> to study two representative tools for debugging. We focus on measuring the observations that the tools enable developers to make (i.e., observability), and derive concrete metrics to measure the observability qualitatively and quantitatively. Our measurement demonstrates the different design tradeoffs in terms of debugging information and overhead. More importantly, we observe that both tools may behave abnormally when applied to diagnose a few tricky cases. Also, we find that neither tool can provide low-level information on how the persistent storage states are changed, which is essential for understanding storage failures. To address the limitation, we develop lightweight extensions to enable such functionality in both tools. We hope that <span><math><mrow><mi>B</mi><mi>u</mi><mi>g</mi><mi>B</mi><mi>e</mi><mi>n</mi><mi>c</mi><msup><mrow><mi>h</mi></mrow><mrow><mi>k</mi></mrow></msup></mrow></math></span> and the enabled measurements will inspire follow-up research in benchmarking and tool support and help address the challenge of failure diagnosis in general.</p></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"1 1","pages":"Article 100006"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772485921000065/pdfft?md5=ccacbd5e0872d394b75b85db5386c9b4&pid=1-s2.0-S2772485921000065-main.pdf","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772485921000065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Diagnosing storage system failures is challenging even for professionals. One recent example is the “When Solid State Drives Are Not That Solid” incident occurred at Algolia data center, where Samsung SSDs were mistakenly blamed for failures caused by a Linux kernel bug. With the system complexity keeps increasing, diagnosing failures will likely become more difficult.
To better understand real-world failures and the potential limitations of state-of-the-art tools, we first conduct an empirical study on 277 user-reported storage failures in this paper. We characterize the issues along multiple dimensions (e.g., time to resolve, kernel components involved), which provides a quantitative measurement of the challenge in practice. Moreover, we analyze a set of the storage issues in depth and derive a benchmark suite called . The benchmark suite includes the necessary workloads and software environments to reproduce 9 storage failures, covers 4 different file systems and the block I/O layer of the storage stack, and enables realistic evaluation of diverse kernel-level tools for debugging.
To demonstrate the usage, we apply to study two representative tools for debugging. We focus on measuring the observations that the tools enable developers to make (i.e., observability), and derive concrete metrics to measure the observability qualitatively and quantitatively. Our measurement demonstrates the different design tradeoffs in terms of debugging information and overhead. More importantly, we observe that both tools may behave abnormally when applied to diagnose a few tricky cases. Also, we find that neither tool can provide low-level information on how the persistent storage states are changed, which is essential for understanding storage failures. To address the limitation, we develop lightweight extensions to enable such functionality in both tools. We hope that and the enabled measurements will inspire follow-up research in benchmarking and tool support and help address the challenge of failure diagnosis in general.
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