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NetProfiler: Profiling Networks From the Edge

NetProfiler: Profiling Networks From the Edge. Venkat Padmanabhan Microsoft Research June 2005 With Sharad Agarwal (MSR), Jitu Padhye (MSR), Dilip Joseph (UCB), Sriram Ramabhadran (UCSD). Motivation: End Users. Users have little info or recourse when they experience network problems

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NetProfiler: Profiling Networks From the Edge

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  1. NetProfiler: Profiling Networks From the Edge Venkat Padmanabhan Microsoft Research June 2005 With Sharad Agarwal (MSR), Jitu Padhye (MSR), Dilip Joseph (UCB), Sriram Ramabhadran (UCSD)

  2. Motivation: End Users Users have little info or recourse when they experience network problems • Why the failure? • website, ISP, client site? • is it just me? • How am I faring over the long term? • switch ISPs?

  3. Motivation: Network Operators Network health? Operators have little visibility into end-user network experience • Enterprise networks: • adequately provisioned? • health of wireless LAN? • Consumer ISPs • how are users in Boston faring? Microsoft AT&T UUNet Sprint MS India MS SVC MS UK

  4. NetProfiler Goal: remedy the situation by leveraging passive observation of normal end-to-end network communication at the “edge” to “profile” the network. Edge = client hosts distributed around the network Profile = monitor + deconstruct (+ diagnose) Turn the Internet into a sensor network

  5. NetProfiler Overview • Key idea: leverage peer cooperation • share network experience info across end hosts • draw inferences based on correlation • Observations • automate what expert users do manually • unlike traditional P2P applications • Complements previous work • network infrastructure monitoring • active probing • server-based monitoring • network tomography

  6. Architecture • Sensing: glean info from existing communication • TCP, web, email, streaming, etc. • quantify the user’s network experience • web download failure, e2e email delay • Aggregation: • based on attributes (website, proxy, domain pair) • tradeoff between privacy and data integrity • Inference: distributed blame attribution • assign credit/blame equally to all entities involved • use mass of info from diverse vantage points to make inference

  7. Measurement Study • Goal: • characterize end-to-end web access failures • make inferences based on shared observations • Testbed: • 134 clients worldwide • academic, corporate, dialup, broadband • 80 websites worldwide • Month-long experiment (Jan ‘05) • synthetic workload: each client downloads top level “index” file from each website ~4 times an hour

  8. Basic Findings • Findings based on local observations • Transaction failure rate: 0.7-2.8% • TCP conn failures: 57-64%, DNS failures: 34-42% • DNS: dominated by LDNS reachability problems (76-83%) • TCP: dominated by conn establishment failures (41-79%) • Correlation analyses to shed more light on the nature of failures • Server-side or client-side • Proxy-related

  9. Classification of Connection Failures Connection failures are dominated by server-side problems

  10. End-to-End Failures vs. BGP Instability Severe BGP instability is rare but has E2E impact when it happens.

  11. Proxy-related Problem Server: www.iitb.ac.in Clients behind proxy see significantly higher failure rate

  12. Conclusion • NetProfiler leverages edge perspective to monitor network health & infer cause of problems • Targeted at both end users and operators • More info: www.research.microsoft.com/~padmanab/projects/NetProfiler

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