.png){kind=icon}
.png){kind=icon}
Return
Efficient random walk sampling in distributed networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2015.01.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Performing random walks in networks is a fundamental primitive that has found numerous applications in communication networks such as token management, load balancing, network topology discovery and construction, search, and peer-to-peer membership management. While several such algorithms are ubiquitous, and use numerous random walk samples, the walks themselves have always been performed naively. In this paper, we focus on the problem of performing random walk sampling efficiently in a distributed network. Given bandwidth constraints, the goal is to minimize the number of rounds and messages required to obtain several random walk samples in a continuous online fashion. We present the first round and message optimal distributed algorithms that present a significant improvement on all previous approaches. The theoretical analysis and comprehensive simulations of our algorithms show that they perform very well in different types of networks of differing topologies. In particular, our results show how several random walks can be performed continuously (when source nodes are provided only at runtime, i.e., online), such that each walk of length l can be performed exactly in just (O) over tilde(root lD) rounds(1) (where D is the diameter of the network), and O(l) messages. This significantly improves upon both, the naive technique that requires O(l) rounds and O(l) messages, and the sophisticated algorithm of Das Sarma et al. (2013) that has the same round complexity as this paper but requires Omega(m root l) messages (where m is the number of edges in the network). Our theoretical results are corroborated through extensive simulations on various topological data sets. Our algorithms are fully decentralized, lightweight, and easily implementable, and can serve as building blocks in the design of topologically-aware networks. (C) 2015 Elsevier Inc. All rights reserved.
Keywords:
Random walks
Random sampling
Decentralized computation
Distributed algorithms
Self-aware network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
3.8K
Citations:
4.8K