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Content Provider | ACM Digital Library |
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Author | Doerr, Benjamin Goldberg, Leslie Ann Minder, Lorenz Sauerwald, Thomas Scheideler, Christian |
Abstract | In the standard consensus problem there are n processes with possibly different input values and the goal is to eventually reach a point at which all processes commit to exactly one of these values. We are studying a slight variant of the consensus problem called the stabilizing consensus problem [2]. In this problem, we do not require that each process commits to a final value at some point, but that eventually they arrive at a common, stable value without necessarily being aware of that. This should work irrespective of the states in which the processes are starting. Our main result is a simple randomized algorithm called median rule that, with high probability, just needs O(log m log log n + log n) time and work per process to arrive at an almost stable consensus for any set of m legal values as long as an adversary can corrupt the states of at most √n processes at any time. Without adversarial involvement, just O(log n) time and work is needed for a stable consensus, with high probability. As a by-product, we obtain a simple distributed algorithm for approximating the median of n numbers in time O(log m log log n + log n) under adversarial presence. |
Starting Page | 149 |
Ending Page | 158 |
Page Count | 10 |
File Format | |
ISBN | 9781450307437 |
DOI | 10.1145/1989493.1989516 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2011-06-04 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Self-stabilization Distributed consensus Randomized algorithms |
Content Type | Text |
Resource Type | Article |
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