AN EFFICIENT DISK-BASED TOOL FOR SOLVING LARGE MARKOV-MODELS

Very large Markov models often result when modeling realistic computer systems and networks. We describe an efficient tool for solving gener al, large Markov models on a typical engineering workstation. It uses a disk to hold the state-transition-rate matrix (possibly compressed), a variant of block Gauss-Seidel as the iterative solution method, and an innovative implementation that involves two parallel processes com municating by shared memory. We demonstrate its use on two large, real istic performance models. (C) 1998 Elsevier Science B.V. All rights re served.