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Related Experiment Videos

Using multi-class queuing network to solve performance models of e-business sites.

Xiao-ying Zheng1, De-ren Chen

  • 1College of Computer Science, Zhejiang University, Hangzhou 310027, China. zhengbetty@hotmail.com

Journal of Zhejiang University. Science
|December 10, 2003
PubMed
Summary

Multi-class queuing networks (QN) effectively model e-business performance by analyzing customer behavior. This study presents efficient algorithms for solving these complex networks, demonstrating their accuracy and applicability.

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Area of Science:

  • Operations Research
  • Computer Science
  • E-commerce Analytics

Background:

  • E-business environments feature diverse customers with varied demands and navigation patterns.
  • Traditional queuing network (QN) models require adaptation to accurately represent e-business complexities.

Purpose of the Study:

  • To establish multi-class queuing networks (QN) as a suitable performance model for e-business.
  • To present efficient algorithms for solving both open and closed multi-class QN models relevant to e-business.

Main Methods:

  • Utilizing established formulas for calculating performance measures like throughput and response time in open QN models.
  • Employing an approximate mean value analysis (MVA) algorithm for closed QN models due to computational constraints of exact methods.

Related Experiment Videos

  • Adapting mixed QN models by eliminating open classes to enable the application of closed model algorithms.

    • Main Results:

    • Demonstrated that multi-class QN models provide a reasonably accurate representation of e-business performance.
    • Showcased efficient computational techniques for solving complex multi-class QN models.
    • Validated the practical application of these QN models through relevant examples.

    Conclusions:

    • Multi-class queuing networks are a viable and accurate modeling approach for e-business systems.
    • Efficient algorithms, particularly approximate MVA, make the analysis of these complex models computationally feasible.
    • The presented methods offer practical solutions for understanding and optimizing e-business performance.