Deconstructing internet paths: an approach for AS-level detour route discovery
Deconstructing internet paths: an approach for AS-level detour route discovery
复制标题
解构互联网路径：AS级绕行路由发现方法
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
P. Pietzuch
中科院分区：
文献类型：
--
作者：
Sing Wang Ho;T. Haddow;J. Ledlie;M. Draief;P. Pietzuch
绕行路径为覆盖网络提供了更好的性能和弹性。用扩展到数百万个节点的方法找到好的绕行路线是一个具有挑战性的问题。我们提出了一种分散发现绕行路径的新方法，该方法基于观察到穿越重叠自治系统集的互联网路径可能受益于相同的绕行节点。我们展示了节点如何在自治系统级别学习互联网路径之间的重叠，并展示了它们如何利用其他节点已经发现的弯路。我们的方法是根据自治系统在节点之间穿越重叠和潜在弯路的程度来聚类路径。我们发现我们的集中路径聚类算法在PlanetLab的176个节点数据集中正确分类了90%以上的潜在延迟弯路。在我们的去中心化版本中，每个节点仅从10%的其他节点采样数据，我们检测到60%的潜在可用弯路。
Detour paths provide overlay networks with improved performance and resilience. Finding good detour routes with methods that scale to millions of nodes is a challenging problem. We propose a novel approach for decentralised discovery of detour paths based on the observation that Internet paths that traverse overlapping sets of autonomous systems may benefit from the same detour nodes. We show how nodes can learn about overlap between Internet paths at the level of autonomous systems and demonstrate how they can exploit detours that other nodes have already found. Our approach is to cluster paths based on the extent to which the autonomous systems traversed overlap and gossip potential detours among nodes. We find that our centralised path clustering algorithm correctly classified over 90% of potential latency detours in a 176-node dataset drawn from PlanetLab. In our decentralised version, we detected 60% of potentially available detours with each node sampling data from only 10% of other nodes.