An Optimal Bit Complexity Randomized Distributed MIS Algorithm (Extended Abstract)
We present a randomized distributed maximal independent set (MIS) algorithm for arbitrary graphs of size n that halts in time O(logn) with probability 1 − o(n − 1), each message containing 1 bit: thus its bit complexity per channel is O(logn) (the bit complexity is the number of bits we need to solve a distributed task, it measures the communication complexity). We assume that the graph is anonymous: unique identities are not available to distinguish the processes; we only assume that each vertex distinguishes between its neighbours by locally known channel names. Furthermore we do not assume that the size (or an upper bound on the size) of the graph is known. This algorithm is optimal (modulo a multiplicative constant) for the bit complexity and improves the best previous randomized distributed MIS algorithms (deduced from the randomized PRAM algorithm due to Luby [Lub86]) for general graphs which is O(log2 n) per channel (it halts in time O(logn) and the size of each message is logn). This result is based on a powerful and general technique for converting unrealistic exchanges of messages containing real numbers drawn at random on each vertex of a network into exchanges of bits. Then we consider a natural question: what is the impact of a vertex inclusion in the MIS on distant vertices? We prove that this impact vanishes rapidly as the distance grows for bounded-degree vertices. We provide a counter-example that shows this result does not hold in general. We prove also that these results remain valid for Luby’s algorithm presented by Lynch [Lyn96] and by Wattenhofer [Wat07]. This question remains open for the variant given by Peleg [Pel00].
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