Measurement and Modeling of Packet Loss in the Internet

1. Measurement and Modeling of Packet Loss in the Internet Maya Yajnik

2. Overview • Context and motivation • Contributions of my thesis • Loss in the MBone multicast network • Temporal correlation of loss • Accuracy of loss measurements • Summary

3. Network Protocol Design • Providing reliability, congestion control, flow control for • multimedia applications • multicast networking • Multimedia traffic in the Internet • streaming multimedia: web-based audio/ video clips • interactive multimedia: Internet telephony, audio/video conferencing

4. Multicast Networking • allows group communication • application: audio/ video conferencing • MBone: multicast backbone overlaid over the Internet • experimental testbed for application design

5. Understanding underlying network behavior leads to informed design choices Observations and models useful in analysis and simulation of performance of network protocols Useful to characterize general patterns of network behavior find where in the network impairments occur detect anomalous behavior Why measure and model loss?

6. Contributions of My Thesis • Loss in the MBone multicast network: • estimated where loss occurs in the network • modeled spatial correlation in loss • characterized loss bursts • Temporal correlation of loss: • estimated correlation timescale of loss • modeled temporal correlation in loss • Accuracy of probe loss measurements: • found they capture congestion level • found they do not capture overall loss rate

7. Measurement of Loss in MBone • Sender transmits audio data at regular intervals • Data collecting programs at receivers give end-end behavior • 17 geographically distributed receivers • off-line analysis of data

8. Internet Topology Edge Backbone

9. Where does MBone loss occur? Mass. Sweden California • Methodology: • link loss inferred from loss at receivers • correlation of received packets provides glimpse inside • Results: • observable backbone loss small 0.5% 0.1% 0.1% 0.002% 5% Germany 0.1% 0.01% 0.2% 0.01% 0.04% 0.01% Virginia Texas France Wash. 0.4% 0.1% 0.2% 7% 21% 1% 1% 0.4% 16% Cal. France Maryland Kentucky

10. Simultaneous Loss and Models • Models of Spatial Correlation • star topology • full topology • modified star topology

11. Loss Burst Characterization • Question: do losses occur singly or in long bursts? • Results: • mostly singly • occasional long periods of 100% loss lasting 10 seconds to 2 minutes

12. Summary: Multicast Loss • Measured loss at 17 geographically distributed sites in the MBone multicast network • Inferred link loss from loss at receivers • Backbone loss found to be small • Modified star found to be a good model • Most losses occur singly • Occasional long outages

13. Overview • Context and motivation • Contributions of my thesis • Loss in the multicast network • Temporal Correlation of loss • Accuracy of loss measurements • Summary

14. Time Correlation in End-end Loss • Questions: • what is the time correlation of packet loss? • what is good model for the loss process? • Useful for: • design, performance analysis and simulation • adaptive mechanisms for multimedia applications (eg. coding techniques) • on-line loss estimation in multimedia applications

15. 5 4 2 1 5 4 3 2 1 loss Temporal Correlation Internet Observations at the receiver time lag

16. Temporal Correlation Overview • Measurement • Analysis • stationarity • data representation • temporal correlation • modeling • Markov chain models • estimation of order • Summary

17. Measurement Methodology • collected point-point, multicast traces of periodically generated probes • probes sent at regular intervals of 20ms, 40ms, 80ms, 160ms • source: University of Massachusetts Amherst • destinations: Atlanta, Los Angeles, Seattle, St. Louis, Stockholm • 128 hours of data

18. Stationarity • Divided trace into 2 hour segments • Checked for stationarity • look for change in loss average over trace • removed non-stationary sections • Result: selected 76 hours of data

19. Data Representations • binary time series • no loss: 0, loss: 1 • eg. {00011000001} • interleaved sequences of good run lengths, loss run lengths • eg.{ 00011 000001 } {3,5} {2,1} { { { { good lossgood loss

20. Correlation Timescale • goal: time interval between packets, at and beyond which loss events are independent • methodology: • autocorrelation function • 95% bounds around zero for sampling error • chi-square test for independence

21. Correlation Timescale finding: correlation timescale usually 1 second, often < 640ms

22. Run lengths: Correlation • question • are they independent? • methodology • autocorrelation functions, • crosscorrelation function • findings • 160ms traces: • independent • 20ms,40ms traces: • dependent good runs

23. Run lengths: Distributions good run length distribution • question • how are they distributed? • geometrically ? loss run length distribution

24. We propose using k-th order Markov chain models prob. of loss/no loss depends k previous events (i.e. the state) number of states = 2k Previously used: Bernoulli loss (order 0): independent loss 2-state model (order 1): prob. of loss/no loss depends on the previous event 1 0 00 01 1 1 0 1 0 0 10 1 11 0 1 0 1 0 Models order 1 model order 2 model

25. Order of the Markov process For an example 160ms trace correlation timescale = 640ms relevant history order 3 Markov process

26. Models • Question: what is the appropriate order of the Markov process? • the lag beyond which the loss events are “independent” • related to correlation timescale • Results: • 160ms traces: • order 0 (Bernoulli) : 14 hr / 66 hr • order 1 (2-state model): 20 hr/ 66 hr • order 2-6: 32 hr/ 66 hr • 40ms traces: order 10, 14, 22 • 20ms traces: order 17, 42

27. Temporal Correlation Summary • collected/ analyzed 128 hours of loss data • correlation timescale < 1000ms • Markov chain models of k-th order • Bernoulli or 2-state models accurate for aproximately half the data

28. Accuracy of probe loss measurements • Stream of packets “probe” the state of the network (congested or not) • UDP datagram probes Periodic Probes Poisson Probes

29. Accuracy of loss measurements • Questions: • Does probe loss rate reflect congestion level in the network? • Answer: yes • no appreciable difference between periodic and Poisson probes • Does probe loss rate reflect the overall packet loss rate of traffic? • Answer: no

30. Methodology • Network simulation • can record network state and performance • congestion level • probe loss rate • probing intervals 1ms to 100ms • overall packet loss rate • measure of probe performance • normalized difference between probe loss rate and congestion level

31. Simulation Topology • Bottleneck link • 1Mbps and 10Mbps • buffer size of 50 packets • focus on forward direction only • Traffic • TCP sessions • on-off sources

32. Simulation Methodology • Congestion level • average fraction of time bottleneck queue is full • Probe traces • sample state of the queue • binary sequences: eg. 000101010000 • 0: queue is not full, 1: queue is full • no packets sent

33. Sampling network state • baseline periodic samples • baseline Poisson samples • select subset of samples

34. Results • Question: does probe loss rate capture the congestion level? • Measure: Error in probes’ estimation of congestion level

35. Results • Question: Does probe loss rate capture the overall packet loss rate?

36. Summary: Accuracy of loss measurements • Questions: • Does probe loss rate reflect congestion level in the network? • Answer: yes • no appreciable difference between periodic and Poisson probes • Does probe loss rate reflect the overall packet loss rate of traffic? • Answer: no

37. Contributions of My Thesis • Loss in the MBone multicast network: • estimated where loss occurs in the network • modeled spatial correlation in loss • characterized loss bursts • Temporal correlation of loss: • estimated correlation timescale of loss • modeled temporal correlation in loss • Accuracy of probe loss measurements: • found they capture congestion level • found they do not capture overall loss rate