Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for Production Cloud Computing Systems
Publication in refereed journal


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摘要Performance diagnosis is labor intensive in production cloud computing systems. Such systems typically face many real-world challenges, which the existing diagnosis techniques for such distributed systems cannot effectively solve. An efficient, unsupervised diagnosis tool for locating fine-grained performance anomalies is still lacking in production cloud computing systems. This paper proposes CloudDiag to bridge this gap. Combining a statistical technique and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of the performance problems, which does not require any domain-specific knowledge to the target system. CloudDiag has been applied in a practical production cloud computing systems to diagnose performance problems. We demonstrate the effectiveness of CloudDiag in three real-world case studies.
著者Mi HB, Wang HM, Zhou YF, Lyu MRT, Cai H
期刊名稱IEEE Transactions on Parallel and Distributed Systems
出版年份2013
月份6
日期1
卷號24
期次6
出版社IEEE COMPUTER SOC
頁次1245 - 1255
國際標準期刊號1045-9219
電子國際標準期刊號1558-2183
語言英式英語
關鍵詞Cloud computing; performance diagnosis; request tracing
Web of Science 學科類別Computer Science; Computer Science, Theory & Methods; COMPUTER SCIENCE, THEORY & METHODS; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2020-25-09 於 14:03