congestion routing else server congestion routing else 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 packetlevel measurment give much information Do not infer problems inside the network What Would Be Cool
Passive Trigger alerts in real time For problems due to Server load Congestion Routing error Common Symptoms Delay and drop TCPs Closed-loop Control
Delays/drops reflected in RTT/RTO estimations 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
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)
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
Road Map Motivation and rationale Mechanism details Conclusion and outlook 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 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 Wavelet Example 1 0 -1 00 00 00 00 11 11 11 11
s1 00002222 00000000 s2 0044 08 s3 8
00 8 0000 d1 d2 d3 Self-similarity
Energy function Ej = (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 and j Self-similar Traffic
Effect of Periodicity self-similar Internet Traffic Adding Periodicity packets arrive periodically, 1 pkt/23 msec coefficients cancel out at scale 4 10 00 00 00 10 00 00 00
s1 10001000 10001000 s2 1010 11 s3
2 11 0 1010 d1 d2 d3
Simulation Traffic Single RTT Simulation Traffic Congestion 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 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
Real Traffic By Subnets Real Traffic By Periods Real Traffic By Periods Detecting Methodology Reference function
Smoothed average Difference Area below the reference function Weighted sum by scale Flagged interesting Top 10% deviations Pick Out Interesting Ones 26, 30, 31
Validation By WIND off-line mode Detailed RTT/RTO estimations Volume Similar heuristics (area difference) CCDF of RTT/RTO Ratio of RTO/RTT Volume
Validate period 26, 30, 31 CCDF of RTT: CCDF of RTO: pick out period 29, 30, 31 pick out period 23, 26, 31 80-90% 80-90% are are validated validated interesting interesting
Road Map Motivation and rationale Mechanism details Conclusion and outlook 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 Outlook Full-fledged diagnosing tool More sophisticated heuristics Use of traceroute data Illustrative examples Using the tool (beta release)
Using the methodology Questions? http://www.tik.ee.ethz.ch/~huang
The author would like to thank Mr. Jason Woods, Mr. Dan Carl, Ms. Courtney Sherwood, Mr. William Beckman, Mr. John DenHerder and Mr. Paul Bui for their assistance in data collection. $39,420 DietGel R/E (without Petri dish) $45,260 DietGel R/E...
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Disclosures. I have no personal financial relationships with commercial interests relevant to this presentation. The views expressed are my own, and do not necessarily represent those of the NIH, NIMH, or the Federal Government
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