Passage-time Computation and Aggregation Strategies for Large Semi-Markov Processes

Provided by: Reed Business Information
Topic: Big Data
Format: WORD
High-level semi-Markov modeling paradigms such as semi-Markov stochastic petri nets and process algebras are used to capture realistic performance models of computer and communication systems but often have the drawback of generating huge underlying semi-Markov processes. Extraction of performance measures such as steady-state probabilities and passage-time distributions therefore relies on sparse matrix-vector operations involving very large transition matrices. Previous papers have shown that exact state-by-state aggregation of semi-Markov processes can be applied to reduce the number of states.

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