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Accelerated reliability analysis for self-healing SONET networks

Recently, a parametric State Reward Markov Model (SRMM/p) has been developed for the reliability and availability analysis of self-healing SONET mesh networks [2]. In this paper, we investigate the factors that affect the run-time complexity of the model presented in [2]. In order to accelerate the reliability and availability analysis, we present an approach that aggregates a set of states in the model based on 2-phase hypoexponential distribution. A comparison of the original and the reduced model, with respect to runtime complexity and accuracy, is carried out by applying the models for the analysis of few complex networks.