An efficient disk-based tool for solving large Markov models

Daniel D. Deavours, William H. Sanders

Research output: Contribution to journalArticlepeer-review


Very large Markov models often result when modeling realistic computer systems and networks. We describe an efficient tool for solving general, 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 communicating by shared memory. We demonstrate its use on two large, realistic performance models.

Original languageEnglish (US)
Pages (from-to)67-84
Number of pages18
JournalPerformance Evaluation
Issue number1
StatePublished - Jun 1998


  • Block Gauss-Seidel
  • Markov models
  • Stochastic Petri nets

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Hardware and Architecture
  • Computer Networks and Communications

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