research-article

Persistent Last-mile Congestion: Not so Uncommon

Authors:

Romain Fontugne,

Anant Shah, and

Kenjiro ChoAuthors Info & Claims

IMC '20: Proceedings of the ACM Internet Measurement Conference

October 2020

Pages 420 - 427

https://doi.org/10.1145/3419394.3423648

Published: 27 October 2020 Publication History

Abstract

Last-mile is the centerpiece of broadband connectivity, as poor last-mile performance generally translates to poor quality of experience. In this work we investigate last-mile latency using traceroute data from RIPE Atlas probes located in 646 ASes and focus on recurrent performance degradation. We find that in normal times probes in only 10% ASes experience persistent last-mile congestion but we recorded 55% more congested ASes during the COVID-19 outbreak. Persistent last-mile congestion is not uncommon, it is usually seen in large eyeball networks and may span years. With the help of CDN access log data, we dissect results for major ISPs in Japan, the most severely affected country in our study, and ascertain bottlenecks in the shared legacy infrastructure.

Supplementary Material

MP4 File (imc2020-269-long_01.mp4)

In this video we present an analysis of persistent last-mile congestion where we found that this type of congestion\r\nappears consistently for Atlas probes located in 10% of monitored ASes, including large eyeball networks. In addition, we recorded persistent last-mile congested probes in 55% more ASes during the COVID-19 outbreak. Our detailed analysis of Japan?s major ISPs with the help of CDN logs confirmed that detected last-mile congestion has a drastic impact on users throughput.\r\n

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Cited By

Cho SWeinberg ZBhattacharya ADai SRauf R(2024)Selection of Landmarks for Efficient Active Geolocation2024 8th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA62044.2024.10559002(1-9)Online publication date: 21-May-2024
https://doi.org/10.23919/TMA62044.2024.10559002
Sharma TMarques JFeamster NMarwell N(2023)A First Look at the Spatial and Temporal Variability of Internet Performance Data in Hyperlocal GeographiesSSRN Electronic Journal10.2139/ssrn.4568668Online publication date: 2023
https://doi.org/10.2139/ssrn.4568668
Martin NDogar F(2023)Divided at the Edge - Measuring Performance and the Digital Divide of Cloud Edge Data CentersProceedings of the ACM on Networking10.1145/36291381:CoNEXT3(1-23)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629138
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Index Terms

Persistent Last-mile Congestion: Not so Uncommon
1. Networks
  1. Network performance evaluation
    1. Network measurement
    2. Network performance analysis

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cover image ACM Conferences

IMC '20: Proceedings of the ACM Internet Measurement Conference

October 2020

751 pages

ISBN:9781450381383

DOI:10.1145/3419394

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 October 2020

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October 27 - 29, 2020

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IMC '20 Paper Acceptance Rate 53 of 216 submissions, 25%;

Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

Cho SWeinberg ZBhattacharya ADai SRauf R(2024)Selection of Landmarks for Efficient Active Geolocation2024 8th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA62044.2024.10559002(1-9)Online publication date: 21-May-2024
https://doi.org/10.23919/TMA62044.2024.10559002
Sharma TMarques JFeamster NMarwell N(2023)A First Look at the Spatial and Temporal Variability of Internet Performance Data in Hyperlocal GeographiesSSRN Electronic Journal10.2139/ssrn.4568668Online publication date: 2023
https://doi.org/10.2139/ssrn.4568668
Martin NDogar F(2023)Divided at the Edge - Measuring Performance and the Digital Divide of Cloud Edge Data CentersProceedings of the ACM on Networking10.1145/36291381:CoNEXT3(1-23)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629138
Song XBaltra GHeidemann JMontpetit MLeivadeas AUhlig SJaved M(2023)Inferring Changes in Daily Human Activity from Internet ResponseProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624796(627-644)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624796
Stocker VLehr WSmaragdakis G(2023)COVID-19 and the Internet: Lessons LearnedBeyond the Pandemic? Exploring the Impact of COVID-19 on Telecommunications and the Internet10.1108/978-1-80262-049-820231002(17-69)Online publication date: 9-May-2023
https://doi.org/10.1108/978-1-80262-049-820231002
Nakazato JNakagawa KItoh KFontugne RTsukada MEsaki H(2023)WebRTC over 5 G: A Study of Remote Collaboration QoS in Mobile EnvironmentJournal of Network and Systems Management10.1007/s10922-023-09778-532:1Online publication date: 24-Oct-2023
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Kuang YLi DHuang XZhou M(2022)On the Modeling of RTT Time Series for Network Anomaly DetectionSecurity and Communication Networks10.1155/2022/54990802022Online publication date: 1-Jan-2022
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Kuang YLi DHuang X(2022)KL-DectionWireless Communications & Mobile Computing10.1155/2022/50995082022Online publication date: 1-Jan-2022
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Carisimo EMok RClark DClaffy K(2022)Jitterbug: A New Framework for Jitter-Based Congestion InferencePassive and Active Measurement10.1007/978-3-030-98785-5_7(155-179)Online publication date: 22-Mar-2022
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Nakagawa KTsukada MShima KEsaki H(2021)WebRTC-based measurement tool for peer-to-peer applications and preliminary findings with real usersProceedings of the 16th Asian Internet Engineering Conference10.1145/3497777.3498544(1-8)Online publication date: 14-Dec-2021
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