Performability Analysis Using Semi-Markov Reward Processes

  • Authors:
  • G. Ciardo;R. A. Marie;B. Sericola;K. S. Trivedi

  • Affiliations:
  • -;-;-;-

  • Venue:
  • IEEE Transactions on Computers
  • Year:
  • 1990

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Abstract

M.D. Beaudry (1978) proposed a simple method of computing the distribution of performability in a Markov reward process. Two extensions of Beaudry's approach are presented. The authors generalize the method to a semi-Markov reward process by removing the restriction requiring the association of zero reward to absorbing states only. The algorithm proceeds by replacing zero reward nonabsorbing states by a probabilistic switch; it is therefore related to the elimination of vanishing states from the reachability graph of a generalized stochastic Petri net and to the elimination of fast transient states in a decomposition approach to stiff Markov chains. The use of the approach is illustrated with three applications.