The statistical value of latencies between two sets of hosts over a given period, which is referred as to the statistical latency, can benefit many applications in the next-generation networks, for example, Network-in-a-Box-based resource provisioning. However, the existing methods can hardly achieve low measurement cost and high prediction accuracy simultaneously in large-scale scenarios. In this article, we design a light-weight statistical latency measurement platform named DMS (DNS-based statistical latency Measurement platform at Scale). DMS achieves high measurement accuracy by introducing a metric space to select the closest open recursive DNS (Domain Name System) server to a given host, and predicting the end-to-end latency between two hosts via the measured latency between the two corresponding DNS servers. To reduce the overall measurement overhead, DMS clusters the hosts in the metric space with the open recursive DNS infrastructure in the network as the cluster center, thus achieving low measurement cost and good scalability in large scale simultaneously. To evaluate the performance of DMS, we implement a prototype system in the network. Compared to the widely adopted method King, DMS can reduce the relative error by 18.5% for real-time end-to-end latency prediction and 33% for statistical latency prediction.

中文翻译：

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轻量级大规模统计延迟测量平台


给定时间段内两组主机之间的延迟的统计值，称为统计延迟，可以使下一代网络中的许多应用受益，例如基于Network-in-a-Box的资源分配。然而，现有方法很难在大规模场景下同时实现低测量成本和高预测精度。在本文中，我们设计了一个轻量级的统计延迟测量平台，名为 DMS（基于 DNS 的大规模统计延迟测量平台）。 DMS 通过引入度量空间来选择距离给定主机最近的开放递归 DNS（域名系统）服务器，并通过测量的两个相应 DNS 服务器之间的延迟来预测两个主机之间的端到端延迟，从而实现高测量精度。为了减少整体测量开销，DMS以网络中开放的递归DNS基础设施为聚类中心，对度量空间中的主机进行聚类，从而同时实现低测量成本和良好的大规模可扩展性。为了评估 DMS 的性能，我们在网络中实现了一个原型系统。与广泛采用的方法King相比，DMS可以将实时端到端延迟预测的相对误差降低18.5%，将统计延迟预测的相对误差降低33%。