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  • 标题:Analytic Evaluation on Petri Net by Using Markov Chain Theory to Achieve Optimized Models
  • 本地全文:下载
  • 作者:H. Motameni ; A. Movaghar ; M. Siasifar
  • 期刊名称:World Applied Sciences Journal
  • 印刷版ISSN:1818-4952
  • 电子版ISSN:1991-6426
  • 出版年度:2008
  • 卷号:3
  • 期号:03
  • 出版社:International Digital Organization for Scientific Information Publications
  • 摘要:

    The quality of an architectural design of a software system has a great influence on achieving
    non-functional requirements of a system. A regular software development project is often influenced by
    non-functional factors such as the customers' expectations about the performance and reliability of the
    software as well as the reduction of the underlying risk. The evaluation of non-functional parameters of a
    software system at the early stages of design and its development process are often considered as major
    factors in dealing with these issues. Because these evaluations can help us to choose the most proper model
    which is the securest and the most reliable. In this paper, a method is presented to obtain performance
    parameters from Generalized Stochastic Petri Net (GSPN) to be able to analyze the stochastic behaviour of
    the system. The embedded Continuous Time Markov Chain (CTMC) is derived from the GSPN and the
    Markov chain theory is used to obtain the performance parameters. We have designed a case tool to obtain
    some performance parameters that we discuss about them in this paper in addition to a case study.

  • 关键词:UML Generalized Stochastic Petri Net (GSPN) ; Continuous Time Markov Chain (CTMC) ; Non-Functional Parameters ; Markov Reward Models
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