Probabilistic Analysis of Distributed Processes with Focus on Consensus

Date created: 
2017-09-22
Identifier: 
etd10387
Keywords: 
Stochastic processes
Distributed computing
Consensus
Leader election
Random walks
Social networks
Abstract: 

This thesis is devoted to the study of stochastic decentralized processes. Typical examples in the real world include the dynamics of weather and temperature, of traffic, the way we meet our friends, etc. We take the rich tool set from probability theory for the analysis of Markov Chains and employ it to study a wide range of such distributed processes: Forest Fire Model (social networks), Balls-into-Bins with Deleting Bins, and fundamental consensus dynamics and protocols such as the Voter Model, 2-Choices, and 3-Majority.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
Petra Berenbrink
Claire Mathieu
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) Ph.D.
Statistics: