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A Non-intrusive , Wavelet-based Approach To Detecting Network Performance Problems

A Non-intrusive , Wavelet-based Approach To Detecting Network Performance Problems. Polly Huang ETH Zurich Anja Feldmann U. Saarbruecken Walter Willinger AT&T Labs-Research. Road Map. Motivation and rationale Mechanism details Conclusion and outlook. Web. TCP. Network.

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A Non-intrusive , Wavelet-based Approach To Detecting Network Performance Problems

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  1. A Non-intrusive, Wavelet-basedApproach To Detecting Network Performance Problems Polly Huang ETH Zurich Anja Feldmann U. Saarbruecken Walter Willinger AT&T Labs-Research

  2. Road Map • Motivation and rationale • Mechanism details • Conclusion and outlook

  3. Web TCP Network Link/Physical Performance Problem Web Web TCP TCP Google.com Internet Network Network Link/Physical Link/Physical server proxy congestion congestion routing routing else else

  4. Current State • Active probing • Ex: traceroute, ping • Disturbing - injecting unnecessary traffic • Biasing - distort metrics of interest • ‘Heisenberg’ effects • Passive measurements • Ex: Cisco NetFlow, IP Accounting, other packet-level measurment • give much information • Do not infer problems inside the network

  5. What Would Be Cool • Passive • Trigger alerts in real time • For problems due to • Server load • Congestion • Routing error • Common Symptoms • Delay and drop

  6. TCP’s Closed-loop Control • Delays/drops reflected in RTT/RTOestimations • RTT: round trip time • RTO: retransmission timeout • Quality of Network Path • Values of RTT/RTO estimations • Amounts of RTT/RTO samples • Can be measured passively

  7. Detailed Estimation • Methodology • A hash table of all data packets observed • One RTT sample per data-ack pair • One RTO sample per data-data pair • Slow • ~ #packets/observation period • especially with high date rate connections (the likely trouble makers)

  8. Objectives • Passive measurement • Non-intrusive • Infer quality of network paths • Detecting network performance problem • Efficiently (so can be done in real time) • Wavelet-based technique

  9. Road Map • Motivation and rationale • Mechanism details • Conclusion and outlook

  10. Wavelet-based Technique • Theoretical ground • Wavelet transform • Energy plots (or scaling plots) • Interpreting energy plots • WIND, the problem detection tool • Features & examples • Detection methodology • Validation effort

  11. Theoretical Ground • FFT • Frequency decomposition • fj, Fourier coefficient • Amount of the signal in frequency j • WT: wavelet transform • Frequency (scale) and time decomposition • dj,k, wavelet coefficient • Amount of the signal in frequency j, time k

  12. 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 8 0 0 8 8 Wavelet Example 1 0 -1 00 00 00 00 11 11 11 11 s1 s2 s3 s4 d1 d2 d3 d4

  13. Self-similarity • Energy function • Ej = Σ(dj,k)2/Nj • Self-similar process • Ej = 2j(2H-1) C <- the magic!! • log2 Ej = (2H-1)j + log2C • linear relationship between log2 Ej andj

  14. Self-similar Traffic

  15. Effect of Periodicity self-similar Internet Traffic

  16. 10 00 00 00 10 00 00 00 s1 s2 s3 s4 d1 d2 d3 d4 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 1 2 0 Adding Periodicity • packets arrive periodically, 1 pkt/23 msec • coefficients cancel out at scale 4

  17. Simulation TrafficSingle RTT

  18. Simulation TrafficCongestion

  19. Interpreting Energy Functions • Abrupt knees at • RTT time scale • RTO time scale • Knee shifts • RTT/RTO time changes • Low energy level (after normalization) • congestion • low traffic volume

  20. WIND - The Detection Tool Wavelet-based Inference for Network Detection • Based on libpcap and tcpdump • On-line mode (efficient) • Per packet: compute dj,k • Per observation period: output Ej • On a subnet basis • Off-line mode • Detailed RTT/RTO estimation

  21. Real TrafficBy Subnets

  22. Real TrafficBy Periods

  23. Real TrafficBy Periods

  24. Detecting Methodology • Reference function • Smoothed average • Difference • Area below the reference function • Weighted sum by scale • Flagged interesting • Top 10% deviations

  25. Pick Out Interesting Ones26, 30, 31

  26. Validation By • WIND off-line mode • Detailed RTT/RTO estimations • Volume • Similar heuristics (area difference) • CCDF of RTT/RTO • Ratio of RTO/RTT • Volume

  27. Validate period 26, 30, 31 CCDF of RTT: pick out period 29, 30, 31 CCDF of RTO: pick out period 23, 26, 31 80-90% are validated interesting

  28. Road Map • Motivation and rationale • Mechanism details • Conclusion and outlook

  29. Summary • Detect problems using energy plots • If self-similar, clean linear relationship • If periodic, getting knees • If problems, knee shifts or low energy level • WIND: the online/offline analysis tool • Passive • Efficient

  30. Outlook • Full-fledged diagnosing tool • More sophisticated heuristics • Use of traceroute data • Illustrative examples • Using the tool (beta release) • Using the methodology

  31. Questions? • http://www.tik.ee.ethz.ch/~huang

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