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Measuring Latency Variation in the Internet
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)ORCID iD: 0000-0001-5241-6815
SICS.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)
2016 (English)In: Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies, 2016, 473-480 p.Conference paper (Refereed)
Abstract [en]

We analyse two complementary datasets to quantify the latency variation experienced by internet end-users: (i) a large-scale active measurement dataset (from the Measurement Lab Network Diagnostic Tool) which shed light on long-term trends and regional differences; and (ii) passive measurement data from an access aggregation link which is used to analyse the edge links closest to the user.

The analysis shows that variation in latency is both common and of significant magnitude, with two thirds of samples exceeding 100\,ms of variation. The variation is seen within single connections as well as between connections to the same client. The distribution of experienced latency variation is heavy-tailed, with the most affected clients seeing an order of magnitude larger variation than the least affected. In addition, there are large differences between regions, both within and between continents. Despite consistent improvements in throughput, most regions show no reduction in latency variation over time, and in one region it even increases.

We examine load-induced queueing latency as a possible cause for the variation in latency and find that both datasets readily exhibit symptoms of queueing latency correlated with network load. Additionally, when this queueing latency does occur, it is of significant magnitude, more than 200\,ms in the median. This indicates that load-induced queueing contributes significantly to the overall latency variation.

Place, publisher, year, edition, pages
2016. 473-480 p.
Keyword [en]
Latency, Bufferbloat, Access Network Performance
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-46999DOI: 10.1145/2999572.2999603ISBN: 978-1-4503-4292-6 (print)OAI: oai:DiVA.org:kau-46999DiVA: diva2:1043933
Conference
ACM CoNEXT 2016
Projects
SIDUS READY
Funder
Knowledge Foundation, 317700
Available from: 2016-11-01 Created: 2016-11-01 Last updated: 2016-12-15Bibliographically approved
In thesis
1. On the Bleeding Edge: Debloating Internet Access Networks
Open this publication in new window or tab >>On the Bleeding Edge: Debloating Internet Access Networks
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As ever more devices are connected to the internet, and applications turn ever more interactive, it becomes more important that the network can be counted on to respond reliably and without unnecessary delay. However, this is far from always the case today, as there can be many potential sources of unnecessary delay. In this thesis we focus on one of them: Excess queueing delay in network routers along the path, also known as bufferbloat.

We focus on the home network, and treat the issue in three stages. We examine latency variation and queueing delay on the public internet and show that significant excess delay is often present. Then, we evaluate several modern AQM algorithms and packet schedulers in a residential setting, and show that modern AQMs can almost entirely eliminate bufferbloat and extra queueing latency for wired connections, but that they are not as effective for WiFi links. Finally, we go on to design and implement a solution for bufferbloat at the WiFi link, and also design a workable scheduler-based solution for realising airtime fairness in WiFi.

Also included in this thesis is a description of Flent, a measurement tool used to perform most of the experiments in the other papers, and also used widely in the bufferbloat community.

Place, publisher, year, edition, pages
Karlstad: Karlstad University Press, 2016. 20 p.
Series
Karlstad University Studies, ISSN 1403-8099 ; 2016:49
Keyword
Bufferbloat, WiFi, AQM, queueing, network measurement, performance evaluation, fairness
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-47001 (URN)978-91-7063-732-2 (ISBN)
Presentation
2016-12-06, 1B309 (Sjöströmsalen), Karlstads Universitet, Universitetsgatan 2, Karlstad, 13:15 (English)
Opponent
Supervisors
Available from: 2016-11-16 Created: 2016-11-01 Last updated: 2016-11-16Bibliographically approved

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Høiland-Jørgensen, TokeHurtig, PerBrunstrom, Anna
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