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Summary: Efficient Adaptive Collect using Randomization
Hagit Attiya1
, Fabian Kuhn2
, Mirjam Wattenhofer2
, and Roger Wattenhofer2
1
Department of Computer Science, Technion
2
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
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