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Stochasticity of probabilistic systems: Analysis methodologies case-study Anwitaman Datta, Martin Hasler, Karl Aberer
 

Summary: Stochasticity of probabilistic systems: Analysis methodologies case-study
Anwitaman Datta, Martin Hasler, Karl Aberer
{anwitaman.datta, martin.hasler, karl.aberer}@epfl.ch
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
School of Computer and Communication Sciences
CH-1015 Lausanne, Switzerland
Abstract
We do a case study of two different analysis techniques
for studying the stochastic behavior of a randomized sys-
tem/algorithms: (i) The first approach can be broadly
termed as a mean value analysis (MVA), where the evolu-
tion of the mean state is studied assuming that the system al-
ways actually resides in the mean state. (ii) The second ap-
proach looks at the probability distribution function of the
system states at any time instance, thus studying the evolu-
tion of the (probability mass) distribution function (EoDF).
1 Introduction
Designing large-scale distributed systems in a decentral-
ized setting often relies heavily on randomized algorithms
and self-organization. There are several interesting and crit-

  

Source: Aberer, Karl - Faculté Informatique et Communications, Ecole Polytechnique Fédérale de Lausanne

 

Collections: Computer Technologies and Information Sciences