The Internet Is Not a Big Truck: Toward Quantifying Network Neutrality

Beverly, Robert; Bauer, Steven; Berger, Arno

doi:10.1007/978-3-540-71617-4_14

Cited by 19 publications

(16 citation statements)

References 3 publications

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“…Existing mechanisms for detecting ISP discrimination actively probe ISPs to test for specific cases: Glasnost detects spurious TCP reset packets of BitTorrent connections [8], Beverly et al study port-blocking [24], and NVLens detects prioritization by observing the type-of-service field in ICMP time-exceeded messages [29]. These tools detect specific classes of discrimination, but they have several drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Detecting network neutrality violations with causal inference

Tariq

Motiwala

Feamster

et al. 2009

Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies

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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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Section: Introductionmentioning

confidence: 99%

Detecting network neutrality violations with causal inference

Tariq

Motiwala

Feamster

et al. 2009

Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies

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“…In a subsequent work the same authors illustrated the use of Gnutella in anonymously downloading a Web file [4]. Close to our work, Beverly et al in [7] used the Gnutella Network to quantify the prevalence of port blocking from ISPs and institutions. In their setup, a super-peer in the Gnutella network was instrumented to redirect each contacting client to a specific port of a measurement host controlled by the authors.…”

Section: Related Workmentioning

confidence: 67%

“…In our experiments we use the port numbers defined as "Ports of Interest" in [7]. These ports span a large number of applications (web, p2p, e-mail, games, chat etc.).…”

Section: Network Neutralitymentioning

confidence: 99%

MOR: Monitoring and Measurements through the Onion Router

Antoniades

Markatos

Dovrolis

2010

Passive and Active Measurement

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Abstract. A free and easy to use distributed monitoring and measurement platform would be valuable in several applications: monitoring network or server infrastructures, performing research experiments using many ISPs and test nodes, or checking for network neutrality violations performed by service providers. In this paper we present MOR, a technique for performing distributed measurement and monitoring tasks using the geographically diverse infrastructure of the Tor anonymizing network. Through several case studies, we show the applicability and value of MOR in revealing the structure and function of large hosting infrastructures and detecting network neutrality violations. Our experiments show that about 7.5% of the tested organizations block at least one popular application port and about 5.5% of them modify HTTP headers.

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“…One recent study leverages active measurement launched from end-host to identify traffic differentiation using port blocking [8]. Evidence of differentiation against P2P traffic has been found in many broadband ISPs by BTTest [13].…”

Section: Related Workmentioning

confidence: 99%

“…Researchers have proposed various techniques for detecting traffic differentiation. Beverly et al presented one of the first measurement studies of port blocking behavior from the edge of the Internet [8]. POPI is another tool for determining router traffic differentiation policy based on port numbers via end-host measurements [19].…”

Section: Introductionmentioning

confidence: 99%

Detecting traffic differentiation in backbone ISPs with NetPolice

Zhang

Mao

Zhang

2009

Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement

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Traffic differentiations are known to be found at the edge of the Internet in broadband ISPs and wireless carriers [13,2]. The ability to detect traffic differentiations is essential for customers to develop effective strategies for improving their application performance. We build a system, called NetPolice, that enables detection of content-and routing-based differentiations in backbone ISPs. NetPolice is easy to deploy since it only relies on loss measurement launched from end hosts. The key challenges in building NetPolice include selecting an appropriate set of probing destinations and ensuring the robustness of detection results to measurement noise.We use NetPolice to study 18 large ISPs spanning 3 major continents over 10 weeks in 2008. Our work provides concrete evidence of traffic differentiations based on application types and neighbor ASes. We identify 4 ISPs that exhibit large degree of differentiation on 4 applications and 10 ISPs that perform previous-AS hop based differentiation, resulting in up to 5% actual loss rate differences. The significance of differences increases with network load. Some ISPs simply differentiate traffic based on port numbers irrespective of packet payload and the differentiation policies may only be partially deployed within their networks. We also find strong correlation between performance differences and Type-of-Service value differences in the traffic.

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The Internet Is Not a Big Truck: Toward Quantifying Network Neutrality

Cited by 19 publications

References 3 publications

Detecting network neutrality violations with causal inference

Detecting network neutrality violations with causal inference

MOR: Monitoring and Measurements through the Onion Router

Detecting traffic differentiation in backbone ISPs with NetPolice

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