Compositional competitiveness for distributed algorithms

We define a measure of competitive performance for distributed algorithms based on throughput, the number of tasks that an algorithm can carry out in a fixed amount of work. This new measure complements the latency measure of Ajtai et al. [3], which measures how quickly an algorithm can finish tasks that start at specified times. The novel feature of the throughput measure, which distinguishes it from the latency measure, is that it is compositional: it supports a notion of algorithms that are competitive relative to a class of subroutines, with the property that an algorithm that is k-competitive relative to a class of subroutines, combined with an l-competitive member of that class, gives a combined algorithm that is kl-competitive. In particular, we prove the throughput-competitiveness of a class of algorithms for collect operations, in which each of a group of n processes obtains all values stored in an array of n registers. Collects are a fundamental building block of a wide variety of shared-memory distributed algorithms, and we show that several such algorithms are competitive relative to collects. Inserting a competitive collect in these algorithms gives the first examples of competitive distributed algorithms obtained by composition using a general construction. An earlier version of this work appeared as “Modular competitiveness for distributed algorithms,” in Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, pages 237–246, Philadelphia, Pennsylvania, 22–24 May 1996. Yale University, Department of Computer Science, 51 Prospect Street/P.O. Box 208285, New Haven CT 06520-8285. Supported by NSF grants CCR-9410228, CCR9415410, CCR-9820888, and CCR-0098078. E-mail: [email protected] Computer Science Division, U. C. Berkeley. Supported in part by an NSF postdoctoral fellowship. E-Mail: [email protected]