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Efficient distributed random walks with applications

Published: 25 July 2010 Publication History

Abstract

We focus on the problem of performing random walks efficiently in a distributed network. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain a random walk sample. We first present a fast sublinear time distributed algorithm for performing random walks whose time complexity is sublinear in the length of the walk. Our algorithm performs a random walk of length l in Õ(√l D) rounds (with high probability) on an undirected network, where D is the diameter of the network. This improves over the previous best algorithm that ran in Õ(l2/3D1/3) rounds (Das Sarma et al., PODC 2009). We further extend our algorithms to efficiently perform k independent random walks in Õ(√kl D + k) rounds. We then show that there is a fundamental difficulty in improving the dependence on l any further by proving a lower bound of Ω(√l/log l + D) under a general model of distributed random walk algorithms. Our random walk algorithms are useful in speeding up distributed algorithms for a variety of applications that use random walks as a subroutine. We present two main applications. First, we give a fast distributed algorithm for computing a random spanning tree (RST) in an arbitrary (undirected) network which runs in Õ(√mD) rounds (with high probability; here m is the number of edges). Our second application is a fast decentralized algorithm for estimating mixing time and related parameters of the underlying network. Our algorithm is fully decentralized and can serve as a building block in the design of topologically-aware networks.

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cover image ACM Conferences
PODC '10: Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
July 2010
494 pages
ISBN:9781605588889
DOI:10.1145/1835698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 July 2010

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Author Tags

  1. decentralized computation
  2. distributed algorithms
  3. mixing time
  4. random sampling
  5. random spanning tree
  6. random walks

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