• DocumentCode
    606359
  • Title

    A Tool for Practical Garbage Collection Analysis in the Cloud

  • Author

    Kejariwal, A.

  • fYear
    2013
  • fDate
    25-27 March 2013
  • Firstpage
    46
  • Lastpage
    53
  • Abstract
    Increasingly more and more web applications are migrating to the cloud owing to higher scalability, low cost, and reduced time-to-market. For example, Amazon Web Services (AWS) hosts PBS, Reddit, Netflix, Zynga. Although the elasticity of cloud enables scaling, both up and down, a cluster in response to the incoming traffic, it makes performance modeling and analysis non-trivial. In the context of Java-based web applications, a key aspect is the performance of the garbage collector (GC). Existing tools for analyzing the performance of a GC are tailored for a single Java process, hence not suitable for use in the cloud. To this end, in this paper we present a tool called {bf Shrek} for analyzing GC performance in the cloud. {bf Shrek} facilitates analysis of GC logs of Java applications deployed across a cluster of hundreds of nodes in the cloud. Further, it supports analytics such as time series analysis of GC performance metrics to determine ``bad&quot, nodes and supports visualization of, for example, promotion rate from the young generation to the old generation. {bf Shrek} has already been used to diagnose performance problems for multiple applications at Netflix.
  • Keywords
    Java; cloud computing; data visualisation; storage management; time series; GC performance analysis; GC performance metrics; Java-based Web applications; Netflix; Shrek; bad node determination; cloud; garbage collector; practical garbage collection analysis; time series analysis; visualization; Cloud computing; Hardware; Java; Time series analysis; Time-frequency analysis; Tuning; Cloud Performance; Garbage Collection; Time Series Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Engineering (IC2E), 2013 IEEE International Conference on
  • Conference_Location
    Redwood City, CA
  • Print_ISBN
    978-1-4673-6473-7
  • Type

    conf

  • DOI
    10.1109/IC2E.2013.13
  • Filename
    6529267