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Detecting network neutrality violations with causal inference

Published: 01 December 2009 Publication History

Abstract

We present NANO, a system that detects when ISPs apply policies that discriminate against specific classes of applications, users, or destinations. Existing systems for detecting discrimination are typically specific to an application or to a particular discrimination mechanism and rely on active measurement tests. Unfortunately, ISPs can change discrimination policies and mechanisms, and they can evade these tests by giving probe traffic higher priority. NANO detects ISP discrimination by passively collecting performance data from clients. To distinguish discrimination from other causes of degradation (e.g., overload, misconfiguration, failure), NANO establishes a causal relationship between an ISP and observed performance by adjusting for confounding factors. NANO agents deployed at participating clients across the Internet collect performance data for selected services and report this information to centralized servers, which analyze the measurements to establish causal relationship between an ISP and performance degradations. We have implemented NANO and deployed clients in a controlled environment on Emulab. We run a combination of controlled experiments on Emulab and wide-area experiments on PlanetLab that show that NANO can determine the extent and criteria for discrimination for a variety of discrimination policies and applications.

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  • (2022)Challenges in Net Neutrality Violation Detection: A Case Study of Wehe Tool and Improvements2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS53615.2022.9668372(506-514)Online publication date: 4-Jan-2022
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      cover image ACM Conferences
      CoNEXT '09: Proceedings of the 5th international conference on Emerging networking experiments and technologies
      December 2009
      362 pages
      ISBN:9781605586366
      DOI:10.1145/1658939
      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: 01 December 2009

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      1. causal inference
      2. network neutrality

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      • (2023)Veritas: Answering Causal Queries from Video Streaming TracesProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604828(738-753)Online publication date: 10-Sep-2023
      • (2023)FairNet: A Measurement Framework for Traffic Discrimination Detection on the InternetIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326337320:4(4097-4109)Online publication date: Dec-2023
      • (2022)Challenges in Net Neutrality Violation Detection: A Case Study of Wehe Tool and Improvements2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS53615.2022.9668372(506-514)Online publication date: 4-Jan-2022
      • (2022)Tools to Monitor NeutralityFrom Net Neutrality to ICT Neutrality10.1007/978-3-031-06271-1_9(157-168)Online publication date: 11-May-2022
      • (2021)Divesting in Socially (Ir)responsible Internet Service ProvidersProceedings of the ACM SIGCOMM 2021 Workshop on Technologies, Applications, and Uses of a Responsible Internet10.1145/3472951.3473504(21-28)Online publication date: 23-Aug-2021
      • (2021)Stealthy DGoS Attack Against Network Tomography: The Role of Active MeasurementsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30709908:2(1745-1758)Online publication date: 1-Apr-2021
      • (2021)Challenges in Net Neutrality Violation Detection: A Case Study of Wehe Tool2021 International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS51098.2021.9352826(169-173)Online publication date: 5-Jan-2021
      • (2020)Weaknesses and Challenges of Network Neutrality Measurement Tools2020 16th International Conference on Network and Service Management (CNSM)10.23919/CNSM50824.2020.9269077(1-5)Online publication date: 2-Nov-2020
      • (2020)Pitfalls of data-driven networkingProceedings of the Workshop on Network Meets AI & ML10.1145/3405671.3405815(42-47)Online publication date: 10-Aug-2020
      • (2020)Characterizing Transnational Internet Performance and the Great Bottleneck of ChinaProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33794794:1(1-23)Online publication date: 5-Jun-2020
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