Distributed MST: A Smoothed Analysis

Distributed MST: A Smoothed Analysis
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分布式 MST:平滑分析

DOI:
10.1145/3369740.3369778
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发表时间:
2020
期刊:
ICDCN 2020: Proceedings of the 21st International Conference on Distributed Computing and Networking
影响因子:
--
通讯作者:
Pham, Nguyen Dinh
Pham, Nguyen Dinh
中科院分区:
--
文献类型:
--
作者:
Chatterjee, Soumyottam;Pandurangan, Gopal;Pham, Nguyen Dinh

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我们研究分布式图算法的平滑分析,重点关注基本的最小生成树(MST)问题。为了研究分布式 MST 的时间复杂度作为输入图“扰动”的函数,我们提出了一个平滑模型,该模型由平滑参数 0 ≤ ϵ(n) ≤ 1 参数化,该参数控制每轮可以添加到输入图 G 的随机边的数量。非正式地,ϵ(n) 是每轮可以将随机边添加到节点的概率(通常是 n 的小函数,例如 n--1/4)。添加的随机边一旦添加,就可以(仅)用于通信。我们在上述平滑模型中显示了分布式 MST 时间复杂度的上限和下限。我们提出了一种分布式算法,以高概率计算 MST 并以 Õ(min{1/√ϵ(n)2O(√log n), D+ √n}) 轮数运行,其中 ϵ 是平滑参数,D 是网络直径,n 是网络大小。为了补充我们的上限,我们还显示了 Ω(min{1/√ϵ(n), D + √n})。我们注意到,除了乘法 2O(√log n) polylog(n) 因子之外,上限和下限基本上匹配。我们的工作可以被视为理解分布式图算法的平滑复杂性的第一步。
We study smoothed analysis of distributed graph algorithms, focusing on the fundamental minimum spanning tree (MST) problem. With the goal of studying the time complexity of distributed MST as a function of the "perturbation" of the input graph, we posit a smoothing model that is parameterized by a smoothing parameter 0 ≤ ϵ(n) ≤ 1 which controls the amount of random edges that can be added to an input graph G per round. Informally, ϵ(n) is the probability (typically a small function of n, e.g., n--¼) that a random edge can be added to a node per round. The added random edges, once they are added, can be used (only) for communication.We show upper and lower bounds on the time complexity of distributed MST in the above smoothing model. We present a distributed algorithm that, with high probability, 1 computes an MST and runs in Õ(min{1/√ϵ(n)2O(√log n), D+ √n}) rounds2 where ϵ is the smoothing parameter, D is the network diameter and n is the network size.To complement our upper bound, we also show a lower bound of Ω(min{1/√ϵ(n), D + √n}). We note that the upper and lower bounds essentially match except for a multiplicative 2O(√log n) polylog(n) factor.Our work can be considered as a first step in understanding the smoothed complexity of distributed graph algorithms.
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