Repository logo
 

Randomized Load Balancing on Networks with Stochastic Inputs

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Cai, L 
Sauerwald, TM 

Abstract

Iterative load balancing algorithms for indivisible tokens have been studied intensively in the past. Complementing previous worst-case analyses, we study an average-case scenario where the load inputs are drawn from a fixed probability distribution. For cycles, tori, hypercubes and expanders, we obtain almost matching upper and lower bounds on the discrepancy, the difference between the maximum and the minimum load. Our bounds hold for a variety of probability distributions including the uniform and binomial distribution but also distributions with unbounded range such as the Poisson and geometric distribution. For graphs with slow convergence like cycles and tori, our results demonstrate a substantial difference between the convergence in the worst- and average-case. An important ingredient in our analysis is a new upper bound on the t-step transition probability of a general Markov chain, which is derived by invoking the evolving set process.

Description

Keywords

random walks, randomized algorithms, parallel computing

Journal Title

Leibniz International Proceedings in Informatics, LIPIcs

Conference Name

44th International Colloquium on Automata, Language and Programming (ICALP 2017)

Journal ISSN

1868-8969

Volume Title

Publisher

Leibniz International Proceedings in Informatics
Sponsorship
European Research Council (679660)