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Scalable Stochastic Modelling for Resilience

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Resilience Assessment and Evaluation of Computing Systems

Abstract

This chapter summarises techniques that are suitable for performance and resilience modelling and analysis of massive stochastic systems. We will introduce scalable techniques that can be applied to models constructed using DTMCs and CTMCs as well as compositional formalisms such as stochastic automata networks, stochastic process algebras and queueing networks. We will briefly show how techniques such as mean value analysis, mean-field analysis, symbolic data structures and fluid analysis can be used to analyse massive models specifically for resilience in networks, communication and computer architectures.

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Notes

  1. 1.

    Packet delay variation is defined as the difference between the shortest and the longest transmission time, where lost packets are ignored.

  2. 2.

    Since there are only two distinct actions in the LTS, one bit would be enough to encode the action. However, the encoding \(0\) is often reserved for the special internal action \(\tau \), and in any case it is not mandatory to use the smallest possible number of bits.

Acknowledgments

Jeremy Bradley, Richard Hayden and Nigel Thomas are supported by the UK Engineering and Physical Sciences Research Council on the AMPS project (reference EP/G011737/1). Leïla Kloul is supported by the European Celtic project HOMESNET [8], Philipp Reinecke and Katinka Wolter are supported by the German Research Council under grant Wo 898/3-1.

Author information

Authors and Affiliations

  1. Imperial College, London, UK

    Jeremy T. Bradley & Richard A. Hayden

  2. Department of Applied Information Technology, GU Tech, Oman, Muscat

    Lucia Cloth

  3. Laboratoire PRiSM, Université de Versailles, Versailles, France

    Leïla Kloul

  4. Institute of Computer Science, Free University Berlin, Takustr. 9, 14195 , Berlin, Germany

    Philipp Reinecke & Katinka Wolter

  5. Department of Computer Science, Universität der Bundeswehr München, Neubiberg, Germany

    Markus Siegle

  6. School of Computing Science, Newcastle University, Newcastle, UK

    Nigel Thomas

Authors
  1. Jeremy T. Bradley
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  2. Lucia Cloth
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  3. Richard A. Hayden
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  4. Leïla Kloul
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  5. Philipp Reinecke
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  6. Markus Siegle
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  7. Nigel Thomas
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  8. Katinka Wolter
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Corresponding author

Correspondence to Jeremy T. Bradley .

Editor information

Editors and Affiliations

  1. Inst. Informatik, Humboldt-Universität Berlin, Unter den Linden 6, Berlin, 10099, Berlin, Germany

    Katinka Wolter

  2. Siemens Corporate Research, Princeton, 08540, New Jersey, USA

    Alberto Avritzer

  3. Fac. Ciencias e Tecnologia, Depto. Engenharia Informatica, Universidade de Coimbra, Coimbra, 3030-290, Portugal

    Marco Vieira

  4. Newcastle University, Room 817, Claremont Tower, Newcastle upon Tyne, NE17RU, United Kingdom

    Aad van Moorsel

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© 2012 Springer-Verlag Berlin Heidelberg

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Bradley, J.T. et al. (2012). Scalable Stochastic Modelling for Resilience. In: Wolter, K., Avritzer, A., Vieira, M., van Moorsel, A. (eds) Resilience Assessment and Evaluation of Computing Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29032-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-29032-9_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29031-2

  • Online ISBN: 978-3-642-29032-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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