On Reducing Stretch in Spanning Trees

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Sinchan Sengupta, Sathya Peri
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Abstract

A parameter crucial for preserving the underlying shortest path information in spanning tree construction is called stretch. It is the ratio of the distance of a pair of nodes in the spanning tree to their shortest distance in the graph. In this paper, we present a distributed heuristic LSTree that constructs a Minimum Average Stretch Spanning Tree of an n node $$ n-\mathrm{node} $$ undirected and unweighted graph in 𝒪 ( n ) rounds of the CONGEST model, assuming the nodes know the size of the network. We like to stress that the LSTree protocol is the first use of Betweenness Centrality in constructing low-stretch trees. The heuristic outperforms the current benchmark algorithm of Alon et al. and other spanning tree construction techniques when tested against synthetic and real-world graph inputs. This paper concludes after giving a distributed edge addition technique for building an overlay while reducing the maximum stretch in the spanning tree generated by LSTree. The overlay is a relaxation in the topological requirement, albeit equivalent in functionality to the network backbone. Hence, in this way, the paper considers a holistic view towards building low-stretch spanning trees: reducing both average stretch and max stretch in a single approach.

Abstract Image

关于减少生成树中的伸展
在生成树的构造中,一个对保持底层最短路径信息至关重要的参数被称为拉伸。它是生成树中一对节点的距离与它们在图中的最短距离之比。在本文中,我们提出了一种分布式启发式lstreet,它在CONGEST模型的 (n)轮中构造了一个n -节点的最小平均伸缩生成树$$ n-\mathrm{node} $$无向无权图。假设节点知道网络的大小。我们想强调的是,lstreet协议是在构造低伸缩树时首次使用中间性中心性。当针对合成和真实世界的图输入进行测试时,启发式算法优于Alon等人的当前基准算法和其他生成树构建技术。最后,本文给出了一种分布式边缘加法技术,该技术可以在减少lstreet生成树的最大拉伸的同时构建覆盖层。覆盖层是拓扑需求的放松,尽管在功能上与网络骨干网相同。因此,通过这种方式,本文考虑了构建低拉伸生成树的整体观点:在单一方法中减少平均拉伸和最大拉伸。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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