Approximate steady-state analysis of large Markov models based on the structure of their decision diagram encoding

Authors:
Min Wan;Gianfranco Ciardo;Andrew S. Miner
Affiliations:
Departmentof Computer Science and Engineering, University of California, Riverside, United States;Departmentof Computer Science and Engineering, University of California, Riverside, United States;Departmentof Computer Science, Iowa State University, United States
Venue:
Performance Evaluation
Year:
2011

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Abstract

We propose a new approximate numerical algorithm for the steady-state solution of general structured ergodic Markov models. The approximation uses a state-space encoding based on multiway decision diagrams and a transition rate encoding based on a new class of edge-valued decision diagrams. The new method retains the favorable properties of a previously proposed Kronecker-based approximation, while eliminating the need for a Kronecker-consistent model decomposition. Removing this restriction allows for a greater utilization of event locality, which facilitates the generation of both the state-space and the transition rate matrix, thus extends the applicability of this algorithm to larger and more complex models.