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Using Ising Model to Study Distributed Systems Processing Capability

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Recent Trends in Applied Artificial Intelligence (IEA/AIE 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7906))

Abstract

Quality of service based on distributed systems must be preserved throughout all stages of life cycle. The stage in which this feature is critical is in stage planning of system capacity. Because this is an estimate, the traditional approach is based on the use of queues for capacity calculation. This paper proposes the use of Ising traditional model to capacity study.

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References

  1. Fortier, P., Howard, M.: Computer Systems Performance Evaluation and Prediction. Digital Press (2003)

    Google Scholar 

  2. Menascé, D., Almeida, V., Dowdy, L.: Performance by Design: Computer Capacity Planning by Example. Prentice Hall PTR (2004)

    Google Scholar 

  3. Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Huang, K.: Statistical Mechanics, 2nd edn. Wiley (April 1987)

    Google Scholar 

  5. Greaffeath, D.: Introduction to Random Fields. In: Denumerable Markov Chains, pp. 425–458. Springer, New York (1976)

    Chapter  Google Scholar 

  6. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd edn., p. 364. Cambridge University Press, Cambridge (1992)

    Google Scholar 

  7. Bouman, C.A.: ICIP 1995 Tutorial: Markov Random Fields and Stochastic Image Models (1995)

    Google Scholar 

  8. Peierls, R.E.: On Ising´s model of ferromagnetism. Proc. Camb. Phil. Soc. 32, 477 (1936)

    Article  MATH  Google Scholar 

  9. Besag, J.: Eficiency of pseudo likely-hood estimation for simple Gaussian Fields. Biometrica 64, 616 (1977)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad de Buenos Aires, Argentina

    Facundo Caram, Araceli Proto, Hernán Merlino & Ramón García-Martínez

  2. Grupo de Investigación en Dinámica y Complejidad de la Sociedad de la Información y Grupo de Investigación en Sistemas de Información, Departamento Desarrollo Productivo y Tecnológico, Universidad Nacional de Lanús, Argentina

    Facundo Caram, Araceli Proto, Hernán Merlino & Ramón García-Martínez

Authors
  1. Facundo Caram
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  2. Araceli Proto
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  3. Hernán Merlino
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  4. Ramón García-Martínez
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, 78666, San Marcos, TX, USA

    Moonis Ali

  2. Agent Systems Research Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081, Amsterdam, HV, The Netherlands

    Tibor Bosse

  3. Interactive Intelligence Group, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Koen V. Hindriks  & Catholijn M. Jonker  & 

  4. Computational Intelligence Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Mark Hoogendoorn

  5. Agent Systems Research Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Jan Treur

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© 2013 Springer-Verlag Berlin Heidelberg

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Caram, F., Proto, A., Merlino, H., García-Martínez, R. (2013). Using Ising Model to Study Distributed Systems Processing Capability. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-38577-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38576-6

  • Online ISBN: 978-3-642-38577-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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