Lower bounds for randomized mutual exclusion

Abstract

We establish, for the first time, lower bounds for randomized mutual-exclusion algorithms (with a read-modify-write operation). Our main result is that a constant size shared-variable cannot guarantee strong fairness, even if randomization is allowed. In fact, we prove a lower bound of fl(log log n) bits on the size of the shared-variable, which is also tight. We investigate weaker fairness conditions and derive tight (upper and lower) bounds for them as well. Surprisingly, it turns out that slightly weakening the fairness condition results in an exponential reduction in the size of the required share-variable. Our lower bounds rely on an analysis of Markov-chains, which might be interesting on its own, and may be useful for other applications.