Stochasticity of probabilistic systems: analysis methodologies case-study

Anwitaman Datta; Martin Hasler; Karl Aberer

Workshop on Stochasticity in Distributed Systems

Research Article

Stochasticity of probabilistic systems: analysis methodologies case-study

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1109/COLCOM.2005.1651267,
    author={Anwitaman Datta and Martin Hasler and Karl Aberer},
    title={Stochasticity of probabilistic systems: analysis methodologies case-study},
    proceedings={Workshop on Stochasticity in Distributed Systems},
    publisher={IEEE},
    proceedings_a={STODIS},
    year={2006},
    month={7},
    keywords={Algorithm design and analysis  Distributed computing  Distribution functions  Equations  Information analysis  Large-scale systems  Probability distribution  Steady-state  Stochastic processes  Stochastic systems},
    doi={10.1109/COLCOM.2005.1651267}
}

Anwitaman Datta
Martin Hasler
Karl Aberer
Year: 2006
Stochasticity of probabilistic systems: analysis methodologies case-study
STODIS
ICST
DOI: 10.1109/COLCOM.2005.1651267

Anwitaman Datta¹^,*, Martin Hasler¹^,*, Karl Aberer¹^,*

1: Ecole Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences, CH-1015 Lausanne, Switzerland

*Contact email: anwitaman.datta@epfl.ch, martin.hasler@epfl.ch, karl.aberer@epfl.ch

Abstract

We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state; (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF).

Keywords: Algorithm design and analysis Distributed computing Distribution functions Equations Information analysis Large-scale systems Probability distribution Steady-state Stochastic processes Stochastic systems

Published: 2006-07-24
Publisher: IEEE

: http://dx.doi.org/10.1109/COLCOM.2005.1651267