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Titlebook: Link Reversal Algorithms; Jennifer L. Welch,Jennifer E. Walter Book 2012 Springer Nature Switzerland AG 2012

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书目名称Link Reversal Algorithms
编辑Jennifer L. Welch,Jennifer E. Walter
视频video
丛书名称Synthesis Lectures on Distributed Computing Theory
图书封面Titlebook: Link Reversal Algorithms;  Jennifer L. Welch,Jennifer E. Walter Book 2012 Springer Nature Switzerland AG 2012
描述Link reversal is a versatile algorithm design technique that has been used in numerous distributed algorithms for a variety of problems. The common thread in these algorithms is that the distributed system is viewed as a graph, with vertices representing the computing nodes and edges representing some other feature of the system (for instance, point-to-point communication channels or a conflict relationship). Each algorithm assigns a virtual direction to the edges of the graph, producing a directed version of the original graph. As the algorithm proceeds, the virtual directions of some of the links in the graph change in order to accomplish some algorithm-specific goal. The criterion for changing link directions is based on information that is local to a node (such as the node having no outgoing links) and thus this approach scales well, a feature that is desirable for distributed algorithms. This monograph presents, in a tutorial way, a representative sampling of the work on link-reversal-based distributed algorithms. The algorithms considered solve routing, leader election, mutual exclusion, distributed queueing, scheduling, and resource allocation. The algorithms can be roughly
出版日期Book 2012
版次1
doihttps://doi.org/10.1007/978-3-031-02006-3
isbn_softcover978-3-031-00878-8
isbn_ebook978-3-031-02006-3Series ISSN 2155-1626 Series E-ISSN 2155-1634
issn_series 2155-1626
copyrightSpringer Nature Switzerland AG 2012
The information of publication is updating

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Conclusion,include the .– mutual exclusion problem [41], the publish/subscribe problem [1, 14], implementing simulated annealing [3], coloring an anonymous planar graph [6], and simulating artificial neural network models [6]. Instead, we have focused in depth on a smaller number of fundamental papers to give a foundation on which the reader can build.
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2155-1626 common thread in these algorithms is that the distributed system is viewed as a graph, with vertices representing the computing nodes and edges representing some other feature of the system (for instance, point-to-point communication channels or a conflict relationship). Each algorithm assigns a vi
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