A Light-Weight Statistical Latency Measurement Platform at Scale

Abstract

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 (NIB) based resource provisioning. However, the existing methods can hardly achieve low measurement cost and high prediction accuracy simultaneously in large-scale scenarios. In this paper, we design a light-weight statistical latency measurement platform named DMS. DMS achieves high measurement accuracy by introducing a metric space to select the closest open recursive DNS 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 realtime end-to-end latency prediction and 33% for statistical latency prediction.