Efficient gossip and robust distributed computation

被引:22
|
作者
Georgiou, C
Kowalski, DR
Shvartsman, AA [1 ]
机构
[1] Univ Connecticut, Dept Comp Sci & Engn, Storrs, CT 06269 USA
[2] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
[3] Univ Cyprus, Dept Comp Sci, CY-1678 Nicosia, Cyprus
[4] Univ Warsaw, Inst Informat, PL-02097 Warsaw, Poland
基金
美国国家科学基金会;
关键词
distributed algorithms; processor failures; gossip; performing work; combinatorial tools;
D O I
10.1016/j.tcs.2005.05.019
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper presents an efficient deterministic gossip algorithm for p synchronous, crash-prone, message-passing processors. The algorithm has time complexity T = O(log(2)p) and message complexity M = O(p(1+epsilon)), for any epsilon > 0. This substantially improves the message complexity of the previous best algorithm that has M = O(p(1.77)), while maintaining the same time complexity. The strength and utility of the new result is demonstrated by constructing a deterministic algorithm for performing n tasks in this distributed setting. Previous solutions used coordinator or check-pointing approaches, immediately incurring a work penalty Omega(n + f (.) p) for f crashes, or relied on strong communication primitives, such as reliable broadcast, or had work too close to the trivial Theta(p (.) n) bound of oblivious algorithms. The new algorithm uses p crash-prone processors to perform n similar and idempotent tasks so long as one processor remains active. The work of the algorithm is W = O(n + p (.) min {f + 1, log(3)p}) and its message complexity is M = O(fp(epsilon) + p min {f + 1, log p}), for any epsilon > 0. This substantially improves the work complexity of previous solutions using simple point-to-point messaging, while "meeting or beating" the corresponding message complexity bounds. The new algorithms use communication graphs and permutations with certain combinatorial properties that are shown to exist. The algorithms are correct for any permutations, and in particular, the same expected bounds can be achieved using random permutations. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:130 / 166
页数:37
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