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Distributed Backup Placement in One Round and its Applications to Maximum Matching Approximation and Self-Stabilization

机译:分布式备份放置在一轮中及其应用于最大匹配近似和自稳定化

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In the distributed backup-placement problem each node of a network has to select one neighbor, such that the maximum number of nodes that make the same selection is minimized. This is a natural relaxation of the perfect matching problem, in which each node is selected just by one neighbor. Previous (approximate) solutions for backup placement are non-trivial, even for simple graph topologies, such as dense graphs. In this paper we devise an algorithm for dense graph topologies, including unit disk graphs, unit ball graphs, line graphs, graphs with bounded diversity, and many more. Our algorithm requires just one round, and is as simple as the following operation. Consider a circular list of neighborhood IDs, sorted in an ascending order, and select the ID that is next to the selecting vertex ID. Surprisingly, such a simple one-round strategy turns out to be very efficient for backup placement computation in dense networks. Not only that it improves the number of rounds of the solution, but also the approximation ratio is improved by a multiplicative factor of at least 2. Our new algorithm has several interesting implications. In particular, it gives rise to a (2 + ε)-approximation to maximum matching within O(log* n) rounds in dense networks. The resulting algorithm is very simple as well, in sharp contrast to previous algorithms that compute such a solution within this running time. Moreover, these algorithms are applicable to a narrower graph family than our algorithm. For the same graph family, the best previously-known result has O(log Δ + log* n) running time. Another interesting implication is the possibility to execute our backup placement algorithm as-is in the self-stabilizing setting. This makes it possible to simplify and improve other algorithms for the self-stabilizing setting, by employing helpful properties of backup placement.
机译:在分布式备份 - 放置问题中,网络的每个节点必须选择一个邻居,使得使得相同选择的最大节点数量最小化。这是完美的匹配问题的自然放松,其中每个节点都被一个邻居选择。备份放置的上一个(近似值)解决方案是非琐碎的,即使对于简单的图形拓扑,例如密集图形。在本文中,我们设计了一种用于密集图拓扑的算法,包括单位盘图,单位球图,线图,具有界限分集的图形等。我们的算法只需一轮,并且与以下操作一样简单。考虑邻居ID的循环列表,按升序排序,然后选择“选择顶点”ID旁边的ID。令人惊讶的是,这种简单的单轮策略结果证明了密集网络中的备份放置计算非常有效。不仅它改善了解决方案的轮次数,而且还通过乘法因子来提高近似值至少为此。我们的新算法具有几个有趣的含义。特别地,它会导致(2 +ε) - 在密集网络中的O(log * n)轮内的最大匹配。结果算法也非常简单,与在此运行时间内计算这种解决方案的先前算法鲜明对比。此外,这些算法适用于比我们的算法更窄的图形系列。对于相同的图形系列,最好的先前已知的结果具有O(日志Δ+ log * n)运行时间。另一个有趣的含义是在自稳定设置中执行我们的备份放置算法的可能性。这使得可以通过采用备份放置的有用属性来简化和改进自稳定设置的其他算法。

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