Using Read$k$ Inequalities to Analyze a Distributed MIS Algorithm
Abstract
Until recently, the fastest distributed MIS algorithm, even for simple graphs, e.g., unoriented trees has been the simple randomized algorithm discovered the 80s. This algorithm (commonly called Luby's algorithm) computes an MIS in $O(\log n)$ rounds (with high probability). This situation changed when Lenzen and Wattenhofer (PODC 2011) presented a randomized $O(\sqrt{\log n}\cdot \log\log n)$round MIS algorithm for unoriented trees. This algorithm was improved by Barenboim et al. (FOCS 2012), resulting in an $O(\sqrt{\log n \cdot \log\log n})$round MIS algorithm. The analyses of these tree MIS algorithms depends on "near independence" of probabilistic events, a feature of the tree structure of the network. In their paper, Lenzen and Wattenhofer hope that their algorithm and analysis could be extended to graphs with bounded arboricity. We show how to do this. By using a new tail inequality for readk families of random variables due to Gavinsky et al. (Random Struct Algorithms, 2015), we show how to deal with dependencies induced by the recent tree MIS algorithms when they are executed on bounded arboricity graphs. Specifically, we analyze a version of the tree MIS algorithm of Barenboim et al. and show that it runs in $O(\mbox{poly}(\alpha) \cdot \sqrt{\log n \cdot \log\log n})$ rounds in the $\mathcal{CONGEST}$ model for graphs with arboricity $\alpha$. While the main thrust of this paper is the new probabilistic analysis via read$k$ inequalities, for small values of $\alpha$, this algorithm is faster than the bounded arboricity MIS algorithm of Barenboim et al. We also note that recently (SODA 2016), Gaffari presented a novel MIS algorithm for general graphs that runs in $O(\log \Delta) + 2^{O(\sqrt{\log\log n})}$ rounds; a corollary of this algorithm is an $O(\log \alpha + \sqrt{\log n})$round MIS algorithm on arboricity$\alpha$ graphs.
 Publication:

arXiv eprints
 Pub Date:
 May 2016
 arXiv:
 arXiv:1605.06486
 Bibcode:
 2016arXiv160506486P
 Keywords:

 Computer Science  Distributed;
 Parallel;
 and Cluster Computing
 EPrint:
 To appear in PODC 2016 as a brief announcement