Efficient distributed random walks with applications

  • Authors:

  • Atish Das Sarma;Danupon Nanongkai;Gopal Pandurangan;Prasad Tetali

  • Affiliations:

  • Georgia Institute of Technology, Atlanta, GA, USA;Georgia Institute of Technology, Atlanta, GA, USA;Nanyang Technological University, Singapore, Singapore;Georgia Institute of Technology, Atlanta, GA, USA

  • Venue:
  • Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
  • Year:
  • 2010

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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.