• DocumentCode
    3575992
  • Title

    Log-based service diagnosis method in cloud

  • Author

    Zhichun Jia ; Xing Xing

  • Author_Institution
    Coll. of Inf. Sci. & Technol., Bohai Univ., Jinzhou, China
  • fYear
    2014
  • Firstpage
    1299
  • Lastpage
    1303
  • Abstract
    Cloud computing is becoming an important and popular platform of service computing for supporting on-demand service sharing in the large-scale distributed systems. One of the difficult challenges in cloud computing is how to deliver the reliable web service composition over unreliable web services around the world. To improve the automated diagnosis capability for service composition in cloud, we present diagnosis architecture and a log-based diagnosis method. By decoupling diagnosis service to share diagnosis resources, our diagnosis architecture could select an optimal diagnosis service according to the faulty type. By converting the successful activity execution logs into the diagnosis model, our diagnosis method can identify the faulty activities based on the differences between correct and observation execution traces. Case study shows that our method is effective in diagnosing service faults in cloud.
  • Keywords
    Web services; cloud computing; fault diagnosis; fault tolerant computing; resource allocation; system monitoring; automated diagnosis capability; cloud computing; diagnosis architecture; diagnosis resource sharing; execution traces; faulty activity; large-scale distributed system; log-based diagnosis method; log-based service diagnosis method; on-demand service sharing; optimal diagnosis service; reliable Web service composition delivery; service computing; service fault diagnosis; successful activity execution logs; Business; Cloud computing; Computational modeling; Computer architecture; Fault diagnosis; History; cloud computing; diagnosis architecture; fault diagnosis; web service composition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Control (ICMC), 2014 International Conference on
  • Print_ISBN
    978-1-4799-2537-7
  • Type

    conf

  • DOI
    10.1109/ICMC.2014.7231762
  • Filename
    7231762