A Light-Weight Statistical Latency Measurement Platform at Scale
A Light-Weight Statistical Latency Measurement Platform at Scale
复制标题
轻量级大规模统计延迟测量平台
DOI：
10.1109/tii.2021.3098796
复制
发表时间：
2021-07
影响因子：
12.3
通讯作者：
Zhan Ma
中科院分区：
文献类型：
--
作者：
Xu Zhang;Geyong Min;Qilin Fan;Hao Yin;Dapeng Wu;Zhan Ma
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.
