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Serial Disk-Based Analysis of Large Stochastic Models

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Validation of Stochastic Systems

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2925))

Abstract

The paper presents a survey of out-of-core methods available for the analysis of large Markov chains on single workstations. First, we discuss the main sparse matrix storage schemes and review iterative methods for the solution of systems of linear equations typically used in disk-based methods. Next, various out-of-core approaches for the steady state solution of CTMCs are described. In this context, serial out-of-core algorithms are outlined and analysed with the help of their implementations. A comparison of time and memory requirements for typical benchmark models is given.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Rashid Mehmood

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  1. Rashid Mehmood
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Editor information

Editors and Affiliations

  1. Institute for Theoretical Computer Science, Technical University Dresden, Germany

    Christel Baier

  2. Centre for Telematics and Information Technology, Faculty for Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O. Box 217, 7500, AE, Netherlands

    Boudewijn R. Haverkort

  3. VASY, INRIA, Grenoble Rhône-Alpes, France

    Holger Hermanns

  4. Software Modeling and Verification, RWTH Aachen University, Germany

    Joost-Pieter Katoen

  5. Institut für Technische Informatik, Universität der Bundeswehr München,  

    Markus Siegle

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Mehmood, R. (2004). Serial Disk-Based Analysis of Large Stochastic Models. In: Baier, C., Haverkort, B.R., Hermanns, H., Katoen, JP., Siegle, M. (eds) Validation of Stochastic Systems. Lecture Notes in Computer Science, vol 2925. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24611-4_7

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  • DOI: https://doi.org/10.1007/978-3-540-24611-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22265-1

  • Online ISBN: 978-3-540-24611-4

  • eBook Packages: Springer Book Archive

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