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%1% 0.4% 16%

21%

0.1%

0.1%

0.01%0.04%

0.5%

5%

CaliforniaMass. Sweden

Germany

TexasVirginia

France

FranceMarylandKentucky

Cal.

Wash.

Simultaneous Loss and Models

• Models of Spatial Correlation– star topology

– full topology– modified star

topology

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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

Title:

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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

45 3 2 1

time lag

Observations at the receiver

45 2 1

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

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Data Representations

• binary time series

– no loss: 0, loss: 1– eg. {00011000001}

• interleaved sequences of good run lengths, loss run lengths – eg.{ 000 11 00000 1 } {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

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Correlation Timescale

finding:correlationtimescale usually 1 second,often < 640ms

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Run lengths: Correlation• question

– are they independent?• methodology

– autocorrelation functions,

– crosscorrelation function• findings

– 160ms traces: • independent

– 20ms,40ms traces:

• dependent good runs

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• question– how are they

distributed?– geometrically ?

good run length distribution

loss run length distribution

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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 = 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

00

10 1

10

1

0

00

0

0

10

01

11

1

1

order 1 model

order 2 model

Order of the Markov process

relevant historyorder 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

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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

Sampling network state

• baseline periodic samples

• baseline Poisson samples

• select subset of samples

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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?

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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