A Light-Weight Statistical Latency Measurement Platform at Scale

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Xu Zhang
  • Geyong Min
  • Qilin Fan
  • Hao Yin
  • Zhan Ma

Detail(s)

Original languageEnglish
Pages (from-to)1186-1196
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume18
Issue number2
Online published26 Jul 2021
Publication statusPublished - Feb 2022
Externally publishedYes

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-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.

Research Area(s)

  • Domain Name System, IP networks, Quality of Service

Citation Format(s)

A Light-Weight Statistical Latency Measurement Platform at Scale. / Zhang, Xu; Min, Geyong; Fan, Qilin; Yin, Hao; Oliver Wu, Dapeng; Ma, Zhan.

In: IEEE Transactions on Industrial Informatics, Vol. 18, No. 2, 02.2022, p. 1186-1196.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review