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Global Computation in a Poorly Connected World: Fast Rumor Spreading with No Dependence on Conductance

机译:全球计算中的全球计算:快速谣言扩散,没有对电导的依赖

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In this paper, we study the question of how efficiently a collection of interconnected nodes can perform a global computation in the GOSSIP model of communication. In this model, nodes do not know the global topology of the network, and they may only initiate contact with a single neighbor in each round. This model contrasts with the much less restrictive LOCAL model, where a node may simultaneously communicate with all of its neighbors in a single round. A basic question in this setting is how many rounds of communication are required for the information dissemination problem, in which each node has some piece of information and is required to collect all others. In the LOCAL model, this is quite simple: each node broadcasts all of its information in each round, and the number of rounds required will be equal to the diameter of the underlying communication graph. In the GOSSIP model, each node must independently choose a single neighbor to contact, and the lack of global information makes it difficult to make any sort of principled choice. As such, researchers have focused on the uniform gossip algorithm, in which each node independently selects a neighbor uniformly at random. When the graph is well-connected, this works quite well. In a string of beautiful papers, researchers proved a sequence of successively stronger bounds on the number of rounds required in terms of the conductance Φ and graph size n, culminating in a bound of O(Φ~(-1) log n). In this paper, we show that a fairly simple modification of the protocol gives an algorithm that solves the information dissemination problem in at most O(D + polylog(n)) rounds in a network of diameter D, with no dependence on the conductance, This is at most an additive polylogarithmic factor from the trivial lower bound of D, which applies even in the LOCAL model. In fact, we prove that something stronger is true: any algorithm that requires T rounds in the LOCAL model can be simulated in O(T+ polylog(n)) rounds in the GOSSIP model. We thus prove that these two models of distributed computation are essentially equivalent.
机译:在本文中,我们研究了互联节点的集合如何有效地在通信的八卦模型中执行全局计算的问题。在此模型中,节点不知道网络的全局拓扑,并且它们可能只在每轮中与单个邻居发起联系。该模型与限制性的本地模型相反,其中节点可以同时在单个圆形中与其所有邻居进行通信。此设置中的基本问题是信息传播问题需要多轮通信,其中每个节点具有一些信息,并且需要收集所有其他信息。在本地模型中,这非常简单:每个节点在每轮中广播其所有信息,并且所需的轮次的数量将等于底层通信图的直径。在八卦模型中,每个节点必须独立选择一个邻居以联系,并且缺乏全局信息使得难以制造任何类型的原则选择。因此,研究人员专注于统一的八卦算法,其中每个节点独立地以随机均匀地选择邻居。当图表连接良好时,这很好。在一系列美丽的论文中,研究人员在导电φ和图表尺寸n所需的圆数上证明了一系列连续更强的界限,在o(φ〜(-1)log n)的边界中。在本文中,我们表明,对协议的相当简单的修改给出了一种算法,其在直径D网络中的最多O(D + Polylog(N))舍入的信息传播问题,而没有对电导的依赖性,这是来自D的普通下限的大多数添加剂积极因素,其即使在本地模型中也适用。事实上,我们证明了更强大的事情:可以在八卦模型中的o(t + polylog(n))中模拟本地模型中需要在本地模型中的任何算法。因此,我们证明这两个分布式计算模型基本上是等效的。

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