Network Troubleshooting from End-Hosts. Renata Teixeira Laboratoire LIP6 CNRS and UPMC Sorbonne Universités

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1 Network Troubleshooting from End-Hosts Renata Teixeira Laboratoire LIP6 CNRS and UPMC Sorbonne Universités

2 Internet problems are hard to troubleshoot DNS? NAT? DHCP???? Net1 Stanford network Is my connection ok? Net2 Net3 Is it google? Is the problem in one of the networks in path? 2

3 Nobody has the full picture to diagnose problems Net1 Net2 Stanford network Net3 End-users can t know what happens in network Network operators can t know user experience 3

4 End-to-end measurements to rescue Available data depends on who you are Network operators: Data from their own network End-users: Data from their machine End-to-end measurements compensate for missing data Network operators: Deploy monitoring hosts End-users: Monitor some paths and cooperate with other users Troubleshooting is mostly manual and ad-hoc 4

5 Goal Automatically troubleshoot network faults and performance disruptions Fault identification: where is the problem? Most common: traceroute Assist in pinpointing the location of the problem How to measure to obtain accurate location? Automatic detection: is there a problem? Detect problems before users Fast detection requires continuous monitoring How to monitor with less overhead? 5

6 Overview Identification Paris traceroute Network tomography for fault diagnosis Detection Minimizing active probing cost Performance problems as perceived by users End-host measurement platform Fathom: A browser-based platform 6

7 Paris traceroute B. Augustin, X. Cuvellier, B. Orgogozo, F. Viger, T. Friedman, M. Latapy, C. Magnien (LIP6) D. Veitch (U. Melbourne)

8 Traceroute Actual path TTL exceeded from A.1 TTL exceeded from B.1 m A.1 A.2 B.1 B.2 A B TTL = 1 t Inferred path m TTL = 2 A.1 B.1 t 8

9 Traceroute under load balancing Actual path A C m L TTL = 2 E t B D Inferred path m L A.1 TTL = 3 C.1 False link Missing nodes and links E.2 t B.1 D.1 9

10 Errors happen even under per-flow load balancing A C Flow 1 m L TTL = 2 Port 2 E t B D TTL = 3 Port 3 Traceroute uses the destination port as identifier Needs to match probe to response Response only has the header of the issued probe 10

11 Paris traceroute: Removing false links Solves the problem with per-flow load balancing Probes to a destination belong to same flow Changes the location of the probe identifier Use the UDP checksum m L A TTL = 2 Port 1 Checksum 2 C TTL = 3 Port 1 Checksum 3 E t B D 11

12 Paris traceroute: Tracing all the paths Port Port Port 1 35 A C m L E t B Port Port 2 Port 4 6 D Adaptive probing strategy Vary flow id of probes Send enough probes to enumerate all interfaces with high confidence 12

13 Implications Paris traceroute used for topology mapping More accurate routes Identification with Paris traceroute Forwarding loops and some reachability problems Traceroute identifies the effect, not the cause A C m L E t B D 13

14 Network tomography for fault diagnosis

15 Fault diagnosis in collaborative environments Faults are persistent reachability problems Correlate measurements from multiple end-hosts 15

16 Binary tomography Given Complete network topology Path reachability Find the smallest set of links that explains bad paths If link is bad, all paths that cross the links are bad Given bad links are uncommon bad t1 bad m t2 good [Duffield, 2006] 16

17 Practical issues with tomography Given Complete network topology Path reachability Find the smallest set of links that explains bad paths If link is bad, all paths that cross the links are bad Given bad links are uncommon Often unknown Hard to measure correlated reachability Links can fail for some paths, but not for all 17

18 Measurement methods for tomography Methods to measure path reachability Failure confirmation Failure aggregation Methods to track topology NetDiagnoser: A binary tomography algorithm Handles path-specific failures (e.g., misconfigurations) Takes advantage of routing information 18

19 Predicting and tracking Internet path changes with I. Cunha (UPMC/Technicolor) D. Veitch (U. Melbourne) C. Diot (Technicolor)

20 Challenges of tracking topology with traceroute Cannot measure fast enough to detect all changes Network and system limitations Accurate measurements require extra probes Identify all paths under load balancing 20

21 Frequent vs. accurate High measurements Accuracy Paris traceroute Traceroute Tracetree Doubletree Low Frequency High 21

22 Approach Observation: Internet paths are mostly stable Current techniques waste probes Probe according to path stability Separate tasks Change detection: lightweight probing for speed Path remapping: accuracy with Paris traceroute 22

23 Contributions NN4: Predicting Internet path changes Distinguish between stable and unstable paths DTrack: Tracking Internet path changes Lightweight probing process to detect changes Allocates more probes to unstable paths 23

24 Predicting path changes Prediction goals Time until the next change Number of changes in a time interval Whether a path will change in a time interval Identify features that can help with prediction Features must be computable from traceroutes Characteristics of the current path Characteristics of the last path change Behavior of the path in the recent past 24

25 Feature selection RuleFit to identify feature importance Fraction of time path active in the past (prevalence) Number of changes in the past Number of previous occurrences of the current path Path age Four most important features carry all the predictive information 25

26 NN4 predictor RuleFit is hard to integrate in other systems NN4 is based on the nearest-neighbor scheme Compute neighbors by partitioning the path feature state-space Prediction computed as the average behavior of all neighbors Prevalence 26 Changes in the past

27 NN4: summary NN4 is lightweight, easy to integrate, and as accurate as RuleFit Prediction is not highly accurate It is possible to distinguish unstable/stable paths 27

28 DTrack Goal: Given a probing budget, detect as many changes as possible Allocates probing rates per path using NN4 s predictions Targets probes along each path Reduce redundant probes at shared links Spread probes over time 28

29 Probe rate allocation Allocate rates that minimize number of missed changes Model changes in each path as a Poisson process Estimate the rate of changes using NN4 Compute missed changes as function of probing rate Probing interval Path changes Time 29

30 Probe targeting overview D3 30 D1 D2

31 DTrack summary Detects more changes than optimized traceroute 2.2 times more changes in trace-driven simulations 5 times more changes in PlanetLab High Paris traceroute DTrack Accuracy Traceroute Tracetree Doubletree Low Frequency High 31

32 Summary Focus: Two steps of network troubleshooting Identification of fault location Detection of faults and performance disruptions Contribution: Measurement methods with practical assumptions To obtain accurate measurements To measure efficiently 32

33 Fathom: A browser-based network measurement platform with M. Dhawan (Rutgers), J. Samuel (UCB), M. Allman, C. Kreibich (ICSI) V. Paxson (UCB/ICSI)

34 End-host measurements are challenging Measurement from end-hosts are vital Researchers to understand Internet Practitioners to diagnose user problems Hard to deploy measurements Developers: Portability, safety Users: need to install new software 34

35 A browser-based measurement Why browser? Flexibility, deployability Ubiquity of browser platform Fathom: Firefox extension Measurement API in JavaScript Web page performance System performance Active measurements 35

36 Thank you! HostView: Measure end-host performance with user feedback Mac OS and Linux HomeNet Profiler: One-shot test Home network configuration WiFi performance Internet access performance 36

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