1. 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. 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. 3 Motivation: Network Operators AT&T Microsoft Sprint UUNe t MS SVC MS UK MS India 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? Network health?

4. 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. 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. 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. 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. 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. 9 Classification of Connection Failures Connection failures are dominated by server-side problems

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

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

12. 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