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Performability analysis using semi-Markov reward processes

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4 Author(s)
Ciardo, G. ; Dept. of Comput. Sci., Duke Univ., Durham, NC, USA ; Marie, R.A. ; Sericola, B. ; Trivedi, K.S.

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

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Computers, IEEE Transactions on  (Volume:39 ,  Issue: 10 )