Congrats to Xu Zhang!
Title: A Light Weight Statistical Latency Measurement Platform at Scale
Abstract: The statistical value of the latencies between two sets of hosts over a given period, which is referred as to statistical latency, can benefit many applications in the next- generation networks, such as Network in a Box (NIB) based resource provisioning. However, 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 corresponding two 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 implemented a prototype system in Mainland China. 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.