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Coordinated Consensus in Dynamic Networks

机译:动态网络中的协调一致

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We study several variants of coordinated consensus in dynamic networks. We assume a synchronous model, where the communication graph for each round is chosen by a worst-case adversary. The network topology is always connected, but can change completely from one round to the next. The model captures mobile and wireless networks, where communication can be unpredictable. In this setting we study the fundamental problems of eventual, simultaneous, and A-coordinated consensus, as well as their relationship to other distributed problems, such as determining the size of the network. We show that in the absence of a good initial upper bound on the size of the network, eventual consensus is as hard as computing deterministic functions of the input, e.g., the minimum or maximum of inputs to the nodes. We also give an algorithm for computing such functions that is optimal in every execution. Next, we show that simultaneous consensus can never be achieved in less than n - 1 rounds in any execution, where n is the size of the network; consequently, simultaneous consensus is as hard as computing an upper bound on the number of nodes in the network. For A-coordinated consensus, we show that if the ratio between nodes with input 0 and input 1 is bounded away from 1, it is possible to decide in time n - Θ((n△)~(1/2)), where △ bounds the time from the first decision until all nodes decide. If the dynamic graph has diameter D, the time to decide is min{O(nD/△), n - Ω(n△/D)}, even if D is not known in advance. Finally, we show that (a) there is a dynamic graph such that for every input, no node can decide before time n-O(△~(0.28)n~(0.72)); and (b) for any diameter D = O(△), there is an execution with diameter D where no node can decide before time Ω(nD/△). To our knowledge, our work constitutes the first study of △-coordinated consensus in general graphs.
机译:我们研究了动态网络中协调共识的几种变体。我们假设一个同步模型,其中每轮的交流图由最坏情况的对手选择。网络拓扑始终处于连接状态,但可以从一个回合完全更改为下一回合。该模型捕获了通信可能无法预测的移动和无线网络。在这种情况下,我们研究了最终,同时和A协调共识的基本问题,以及它们与其他分布式问题的关系,例如确定网络的大小。我们表明,在缺乏网络规模的良好初始上限的情况下,最终共识与计算输入的确定性功能(例如节点的最小或最大输入)一样困难。我们还提供了一种用于计算此类函数的算法,该算法在每次执行中都是最佳的。接下来,我们证明在任何执行中,在少于n-1轮中都不可能实现同时达成共识,其中n是网络的大小;因此,同时达成共识与计算网络中节点数量的上限一样困难。对于A坐标共识,我们表明如果输入0的节点与输入1的节点之间的比率定为1,则可以在时间n-Θ((n△)〜(1/2))处确定,其中△界定了从第一个决策到所有节点决策的时间。如果动态图的直径为D,则即使事先不知道D的决定时间也为min {O(nD /△),n-Ω(n△/ D)}。最后,我们表明(a)有一个动态图,使得对于每个输入,没有任何节点可以在时间n-O(△〜(0.28)n〜(0.72))之前决定; (b)对于任何直径D = O(△),都有一个直径为D的执行,其中没有节点可以在时间Ω(nD /△)之前决定。据我们所知,我们的工作是对一般图中的△坐标共识的首次研究。

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