 
Summary: Stochasticity of probabilistic systems: Analysis methodologies casestudy
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
CH1015 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 largescale distributed systems in a decentral
ized setting often relies heavily on randomized algorithms
and selforganization. There are several interesting and crit
