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Efficient Adaptive Collect using Randomization Hagit Attiya1

Summary: Efficient Adaptive Collect using Randomization
Hagit Attiya1
, Fabian Kuhn2
, Mirjam Wattenhofer2
, and Roger Wattenhofer2
Department of Computer Science, Technion
Department of Computer Science, ETH Zurich
Abstract. An adaptive algorithm, whose step complexity adjusts to the number
of active processes, is attractive for distributed systems with a highly-variable
number of processes. The cornerstone of many adaptive algorithms is an adaptive
mechanism to collect up-to-date information from all participating processes. To
date, all known collect algorithms either have non-linear step complexity or they
are impractical because of unrealistic memory overhead.
This paper presents new randomized collect algorithms with asymptotically op-
timal O(k) step complexity and polynomial memory overhead only. In addition
we present a new deterministic collect algorithm which beats the best step com-
plexity for previous polynomial-memory algorithms.
1 Introduction and Related Work


Source: Attiya, Hagit - Department of Computer Science, Technion, Israel Institute of Technology
Schmid, Stefan - Computer Engineering and Networks Laboratory, Eidgenössische Technische Hochschule Zürich (ETHZ)


Collections: Computer Technologies and Information Sciences