组播树算法中的深度-延迟权衡

Michael T. Helmick, Fred S. Annexstein
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引用次数: 8

摘要

组播树的构造由于需要平衡一些重要的目标而变得复杂,包括:最小化延迟、最小化深度/跳数和限定度。本文研究了用节点间延迟来确定度量的完全图中最小平均延迟的度有界有向生成树的确定问题。特别是,我们着重于测量在度有界生成树上施加深度约束(即跳数界限)时对平均延迟的影响。一般问题是一个众所周知的np困难问题,一些工作已经提出了旨在最小化深度或延迟的近似解决方案。在这项工作中,我们提出了一种新的启发式算法,该算法通过考虑深度和延迟以及它们之间的权衡来改进先前的解决方案。我们的算法被证明改善了理论上的最坏情况近似因子,并且我们在经验评估中证明了改进。我们的实验研究和分析了几种不同的拓扑结构,包括低维随机几何网络、随机过境存根网络和高维超立方体网络。我们展示了如何将我们的解决方案应用于在位置感知点对点覆盖网络中启用多播支持的上下文中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depth-Latency Tradeoffs in Multicast Tree Algorithms
The construction of multicast trees is complicated by the need to balance a number of important objectives, including: minimizing latencies, minimizing depth/hops, and bounding the degree. In this paper, we study the problem of determining a degree-bounded directed spanning tree of minimum average-latency in a complete graph where the inter-node latencies are used to determine a metric. In particular, we focus on measuring the effects on average latency when imposing depth constraints (i.e., bounds on hop count) on degree-bounded spanning trees. The general problem is a well known NP-hard problem, and several works have proposed approximate solutions which aim at minimizing either depth or latency. In this work, we present a new heuristic algorithm which improves upon previous solutions by considering both depth and latency and the tradeoffs between them. Our algorithms are shown to improve the theoretical worst-case approximation factors, and we demonstrate improvements under empirical evaluation. Our experiments examine and analyze several different topologies, including, low-dimensional random geometric networks, random transit-stub networks, and high- dimensional hypercube networks. We show how our solutions can be applied in the context of enabling multicasting support in locality aware peer-to-peer overlay networks.
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