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A Multiple System Performance Monitoring Model for Web Services

  • Yong Yang
  • Dan Luo
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5980)

Abstract

With the exponential growth of the world wide web, web services have becoming more and more popular. However, performance monitoring is a key issue in the booming service-orient architecture regime. Under such loosely coupled and open distributed computing environments, it is necessary to provide a performance monitoring model to estimate the likely performance of a service provider. Although much has been done to develop models and techniques to assess performance of services (i.e. QOSs), most of solutions are based on deterministic performance monitoring value or boolean logic. Intuitively, probabilistic representation could be a more precise and nature way for performance monitoring. In this paper, we propose a Bayesian approach to analyze service provider’s behavior to infer the rationale for performance monitoring in the web service environment. This inference facilitates the user to predict service provider’s performance, based on historical temporal records in a sliding window. Distinctively, it combines evidences from another system (For example, recommendation opinions of third parties) to provide complementary support for decision making. To our best of knowledge, this is the first approach to squeeze a final integrated performance prediction with multiple systems in Web services.

Keywords

Bayesian Network Performance Monitoring Bayesian Network Model Conditional Probability Table Target Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yong Yang
    • 1
  • Dan Luo
    • 1
  • Chengqi Zhang
    • 1
  1. 1.Data Sciences & Knowledge Discovery Lab, Center for Quantum Computation and Intelligent Systems. Faculty of Engineering & Information TechnologyUniversity of TechnologySydney

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