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Detecting Network Neutrality Violations with Causal Inference Mukarram Bin Tariq, Murtaza Motiwala Nick Feamster, Mostafa Ammar Georgia Tech

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Presentation on theme: "Detecting Network Neutrality Violations with Causal Inference Mukarram Bin Tariq, Murtaza Motiwala Nick Feamster, Mostafa Ammar Georgia Tech"— Presentation transcript:

1 Detecting Network Neutrality Violations with Causal Inference Mukarram Bin Tariq, Murtaza Motiwala Nick Feamster, Mostafa Ammar Georgia Tech

2 2 November 6, 2006 The Network Neutrality Debate Users have little choice of access networks. ISPs want to share from monetizable traffic that they carry for content providers.

3 3 Goal: Make ISP Behavior Transparent Our goal: Transparency. Expose performance discrimination to users. Source: Glasnost project

4 4 Existing Techniques are Too Specific Detect specific discrimination methods and policies –Testing for TCP RST packets (Glasnost) –ToS-bits based de-prioritization (NetPolice) Limitations –Brittle: discrimination methods may evolve –Evadable ISP can whitelist certain servers, destinations, etc. ISP can prioritize monitoring probes Active probes may not reflect user performance Monitoring is not continuous

5 5 Main Idea: Detect Discrimination From Passively Collected Data Objective: Establish whether observed degradation in performance is caused by ISP Method: Passively collect performance data and analyze the extent to which an ISP causes this degradation This talk: Design, implementation, evaluation, and deployment of NANO

6 6 Ideal: Directly Estimate Causal Effect Baseline Performance Performance with the ISP Causal Effect = E(Real Throughput using ISP) E(Real Throughput not using ISP) Ground truth values for performance with and without the ISP (treatment variable) Problem: Need both ground truth values observed for same client. These values are typically not available.

7 7 Association = E(Observed Throughput using ISP) E ( Observed Throughput not using ISP) Instead: Estimate Association from Observed Data Observed Baseline Performance Observed Performance with the ISP Problem: Association does not equal causal effect. How to estimate causal effect from association?

8 8 Association is Not Causal Effect Comcast Other ISPs Avg. BitTorrent Throughput 5 kbps 10 kbps Comcast BT Throughput ? Client Setup TimeofDay ContentLocation Why? Confounding variables can confuse inference. Suppose Comcast users observe lower BitTorrent throughput. Can we assume that Comcast is discriminating? No! Other factors (confounders) may correlate with both the choice of ISP and the output variable.

9 9 Strawman: Random Treatment Treat subjects randomly, irrespective of their initial health. Measure association with new outcome. Association converges to causal effect if the confounding variables do not change during treatment. = = 0.55 Treated HHH HS Untreated H SS S S H H H SS S SS α θ Common approach in epidemiology. S = sick H = healthy

10 10 The Internet Does Not Permit Random Treatment Random treatment requires changing ISP. Problems –Cumbersome: Nearly impossible to achieve for large number of users –Does not eliminate all confounding variables (e.g., change of equipment at users home network) Alternate approach: Stratification

11 11 Stratification: Adjusting for Confounders Step 1: Enumerate confounders e.g., setup ={, } Step 2: Stratify along confounder variable values and measure association Association implies causation (no other explanation) HH H HH H HH H SS S H S S SS HH H H S S S S S HH HHH SS SS Treated Baseline Strata Causal Effect ( θ )

12 12 Stratification on the Internet: Challenges What is baseline performance? What are the confounding variables? Which data to use, and how to collect it? How to infer the discrimination method?

13 13 What is the baseline performance? Baseline: Service performance when ISP not used –Need some ISP for comparison Approach: Average performance over other ISPs Limitation: Other ISPs may also discriminate

14 14 What are the confounding variables? Client-side –Client setup: Network Setup, ISP contract –Application: Browser, BT Client, VoIP client –Resources: Memory, CPU, network utilization –Other: Location, number of users sharing home connection Temporal –Diurnal cycles, transient failures

15 15 What data to use; how to collect it? NANO-Agent: Client-side, passive collection –per-flow statistics: throughput, jitter, loss, RST packets –application associated with flow –resource monitoring CPU, memory, network utilization Performance statistics sent to NANO-Server –Monitoring, stratification, inference

16 16 Evaluation: Three Experiments Experiment 1: Simple Discrimination –HTTP Web service –Discriminating ISPs drop packets Experiment 2: Long Flow Discrimination –Two HTTP servers S 1 and S 2 –Discriminating ISPs throttle traffic for S1 or S2 if the transfer exceeds certain threshold Experiment 3: BitTorrent Discrimination –Discriminating ISP maintains list of preferred peers –Higher drop rate for BitTorrent traffic to non-preferred peers

17 17 Experiment Setup Access ISP 5 ISPs in Emulab 2 Discriminating Service Providers PlanetLab nodes HTTP and BitTorrent Discrimination Throttling and dropping Policy with Click router Confounding Variables Server location near servers (West coast nodes) far servers (remaining PlanetLab nodes) Internet D1D2N1N2N3 ~200 PlanetLab nodes ISPs Clients Running NANO-Agent

18 18 Without Stratification, Detecting Discrimination is Difficult Overall throughput distribution in discriminating and non-discriminating ISPs is similar. Simple Discrimination

19 19 Stratification Identifies Discrimination Discriminating ISPs have clearly identifiable causal effect on throughput Neutral ISPs are absolved SimpleLong-FlowBitTorrent

20 20 Implementation and Deployment Implementation –Linux version available –Windows and MacOS versions in progress Now: 27 users –Need thousands for inference Performance dashboard may help attract users Throughput DNS Latency Traffic Breakdown Performance Relative to Other Users

21 21 Summary and Next Steps Internet Service Providers discriminate against classes of users and application traffic today. Need passive approach –ISP discrimination techniques can evolve, or may not be known to users. –Tradeoff: Must be able to enumerate confounders NANO: Network Access Neutrality Observatory –Infers discrimination from passively collected data –Detection succeeds in controlled environments –Deployment in progress. Need more users.

22 22

23 23 NANO Can Infer Discrimination Criteria ISP throttles throughput of a flow larger than 13MB or about 10K packets cum_pkts not_discriminated cum_pkts > > discriminated EvaluationApproach

24 24 Sufficiency of Confounding Variables

25 25 Why Association != Causal Effect? Positive correlation in health and treatment Can we say that Aspirin causes better health? Confounding Variables correlate with both cause and outcome variables and confuse the causal inference Aspirin No Aspirin Healthy 40%15% Not Healthy 10%35% Aspirin Health ? Sleep Diet Other Drugs Age

26 26 Network Neutrality ISPs remain neutral in forwarding traffic irrespective of –Content: voice, video, data –Application: p2p, VoIP, VoD –Participants: Service providers, Google, Hulu, Youtube Discrimination: biased or non-neutral forwarding of traffic Focus of this paper –Detecting and Quantifying Discrimination

27 27 Inferring the Criteria Label data in two classes: –discriminated (-) –non-discriminated (+) Train a decision tree for classification –Rules provide hints about the criteria Criteria: youtube traffic, greater than 1 MB is affected

28 28 Discrimination can take many forms Blocking ports Disrupting connections, e.g., using TCP RST Throttling and prioritizing based on destination or service –Target domains, applications, or content Discriminatory peering –Resist peering with certain content providers

29 29 Causality: An Analogy from Health Epidemiology: study causal relationships between risk factors and health outcome NANO: infer causal relationship between ISP and service performance degradation

30 30 Without Stratification, Detecting Discrimination is Hard Overall throughput distribution in discriminating and non-discriminating ISPs is similar. Server location is confounding. Simple Discrimination Experiment Long Flow Discrimination Experiment

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