An online service-oriented performance profiling tool for cloud computing systems

  • Authors:

  • Haibo Mi;Huaimin Wang;Yangfan Zhou;Michael Rung-Tsong Lyu;Hua Cai;Gang Yin

  • Affiliations:

  • National Lab for Parallel & Distributed Processing, National University of Defense Technology, Changsha, China 410073;National Lab for Parallel & Distributed Processing, National University of Defense Technology, Changsha, China 410073;Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China 518000;Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China 518000;Computing Platform, Alibaba Cloud Computing Company, Hangzhou, China 310000;National Lab for Parallel & Distributed Processing, National University of Defense Technology, Changsha, China 410073

  • Venue:
  • Frontiers of Computer Science: Selected Publications from Chinese Universities
  • Year:
  • 2013

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Abstract

The growing scale and complexity of component interactions in cloud computing systems post great challenges for operators to understand the characteristics of system performance. Profiling has long been proved to be an effective approach to performance analysis; however, existing approaches confront new challenges that emerge in cloud computing systems. First, the efficiency of the profiling becomes of critical concern; second, service-oriented profiling should be considered to support separation-of-concerns performance analysis. To address the above issues, in this paper, we present P-Tracer, an online performance profiling tool specifically tailored for cloud computing systems. P-Tracer constructs a specific search engine that proactively processes performance logs and generates a particular index for fast queries; second, for each service, P-Tracer retrieves a statistical insight of performance characteristics from multi-dimensions and provides operators with a suite of web-based interfaces to query the critical information. We evaluate P-Tracer in the aspects of tracing overheads, data preprocessing scalability and querying efficiency. Three real-world case studies that happened in Alibaba cloud computing platform demonstrate that P-Tracer can help operators understand software behaviors and localize the primary causes of performance anomalies effectively and efficiently.