Measurement and Modeling of Packet Loss in the Internet Maya Yajnik

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

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

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

Why measure and model loss? 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

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

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

Internet Topology Backbone Edge

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

Simultaneous Loss and Models Models of Spatial Correlation –star topology –full topology –modified star topology

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

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

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

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

Temporal Correlation Internet time lag Observations at the receiver 4521 loss

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

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

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

Data Representations binary time series –no loss: 0, loss: 1 –eg. { } interleaved sequences of good run lengths, loss run lengths –eg.{ } {3,5} {2,1} good loss good loss {{{{

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

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

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

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

Models 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 = 2 k Previously used: –Bernoulli loss (order 0): independent loss –2-state model (order 1): prob. of loss/no loss depends on the previous event order 1 model order 2 model

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

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

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

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

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

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

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

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

Sampling network state baseline periodic samples baseline Poisson samples select subset of samples

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

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

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

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