Linearization: Locally self-stabilizing sorting in graphs

M Onus, A Richa, C Scheideler - 2007 Proceedings of the Ninth Workshop on …, 2007 - SIAM
2007 Proceedings of the Ninth Workshop on Algorithm Engineering and Experiments …, 2007SIAM
We consider the problem of designing a distributed algorithm that, given an arbitrary
connected graph G of nodes with unique labels, converts G into a sorted list of nodes. This
algorithm should be as simple as possible and, for scalability, should guarantee a
polylogarithmic runtime as well as at most a polylogarithmic increase in the degree of each
node during its execution. Furthermore, it should be self-stabilizing, that is, it should be able
to eventually construct a sorted list from any state in which the graph is connected. It turns …
Abstract
We consider the problem of designing a distributed algorithm that, given an arbitrary connected graph G of nodes with unique labels, converts G into a sorted list of nodes. This algorithm should be as simple as possible and, for scalability, should guarantee a polylogarithmic runtime as well as at most a polylogarithmic increase in the degree of each node during its execution. Furthermore, it should be self-stabilizing, that is, it should be able to eventually construct a sorted list from any state in which the graph is connected. It turns out that satisfying all of these demands at the same time is not easy.
Our basic approach towards this goal is the so-called linearization technique: each node v repeatedly does the following with its neighbors:
  • for its left (i.e., smaller) neighbors u1, …, uk in the order of decreasing labels, v replaces {v, u1}, …, {v, uk} by {v, u1}, {u1,u2},…, {uk − 1,uk}, and
  • for its right (i.e., larger) neighbors w1, …, w in the order of increasing labels, v replaces {v, w1}, …, {v, w} by {v, w1}, {w1, w2}, …, {wℓ − 1, w}.
As shown in this paper, this technique transforms any connected graph into a sorted list, but there are graphs for which this can take a long time. Hence, we propose several extensions of the linearization technique and experimentally evaluate their performance. Our results indicate that some of these have a polylogarithmic performance, so there is hope that there are distributed algorithms that can achieve all of our goals above.
Society for Industrial and Applied Mathematics
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