measurement and modeling of packet loss in the internet maya yajnik
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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