Examples of Traffic

Video
Video Traffic (High Definition)
30 frames per second
Frame format: 1920x1080 pixels
24 bits per pixel
Required rate: 1.5 Gbps
Required storage: 1 TB per hour
Video uses compression algorithm to reduce bitrate
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MPEG compression
I frames: intra-coded
P frames: predictive
B frames: bi-directional
Group of Pictures (GOP): IBBPBBPBB
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Example: Harry Potter
30 minutes of Harry Potter movie with HD encoding
Codec: H.264 SVC
Resolution: 1920x1088
Frames per second: 24 fps
GOP: IBBBPBBBPBBBPBBB
Frame size (Bytes):
Avgerage: 28,534
Minimum: 109
Maximum: 287,576
Mean Frame Bit Rate (Mbps): 5.48
Peak Frame Bit Rate (Mbps): 55.21
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Harry Potter: 30 minutes
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Harry Potter: 20 seconds
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Harry Potter
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Distribution of packet sizes
Distribution of time gap between packets

Voice
Standard (Pulse Code Modulation) voice encoding:
8000 samples per second (8 kHz)
8 bits per sample
Bit rate: 64 kbps
Better quality with higher sampling rate and larger samples
CD encoding:
44 kHz sampling rate
16 bits per sample
2 channels
Bit rate: 1.4 Mbps
Packet voice collects multiple samples in once packet
Modern voice encoding schemes also use compression and silent suppression
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Skype Voice Call: 6 minutes
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SVOPC encoding, one direction of 2-way call
Dark blue: UDP traffic
Light blue: TCP traffic

Skype Voice Call: 2 seconds
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Skype (UDP traffic only)
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Distribution of packet sizes
Distribution of time gap between packets

Internet Traffic: 10 Gbps link
Data measured from a backbone link of a Tier-1 Internet Service provider
Link measured: Chicago – Seattle
Link rate: 10 Gbps (10 Gigabit Ethernet)
Data measures total (aggregate) traffic of all transmissions on the network
Data shown is 1 second:
~430,000 packets packet transmissions
Average rate: ~3 Gbps
Avg. packet size: 868 Bytes
Min. packet size: 44 Bytes
Max. packet size: 1504 Bytes
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Internet Traffic: 10 Gbps link
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One data point is the traffic in one millisecond

Internet Traffic
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Packet arrivals in a 2μs snapshot:

Internet Traffic: 10 Gbps link
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Distribution of packet sizes
Distribution of time gap between packets

Data Traffic: “Bellcore Traces”
Data measured on an Ethernet network at Bellcore Labs with 10Mbps
Data measures total (aggregate) traffic of all transmissions on the network
Measurements from 1989
One of the first systematic analyses of network measurements
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Data Traffic: 100 seconds
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One data point is the traffic in 100 milliseconds

Packet arrivals: 200 milliseconds
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Bellcore traces
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Distribution of packet sizes
Distribution of time gap between packets

Some background on Lab 1

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“Typical network traffic is not well described by Poisson model”
Lab 1
Lab 1 is about comparing a simple model for network traffic (Poisson traffic) with actual network traffic (LAN traffic, video traffic)
Lab 1 retraces one fo the most fundamental insights of networking research ever:

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Poisson
In a Poisson process with rate λ, the number of events in a time interval
(t, t+τ ], denoted by N(t+τ) – N(t), is given by
In a Poisson process with rate λ, the time between events follows an exponential distribution:

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In the Past…
Before there were packet networks there was the circuit-switched telephone network
Traffic modeling of telephone networks was the basis for initial network models
Assumed Poisson arrival process of new calls
Assumed Poisson call duration
Source: Prof. P. Barford (edited)

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… until early 1990’s
Traffic modeling of packet networks also used Poisson
Assumed Poisson arrival process for packets
Assumed Exponential distribution for traffic
Source: Prof. P. Barford (edited)

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The measurement study that changed everything
Bellcore Traces: In 1989, researchers at (Leland and Wilson) begin taking high resolution traffic traces at Bellcore
Ethernet traffic from a large research lab
100 μ sec time stamps
Packet length, status, 60 bytes of data
Mostly IP traffic (a little NFS)
Four data sets over three year period
Over 100 million packets in traces
Traces considered representative of normal use
Source: Prof. C. Williamson
The data in part 3 of Lab 1 is a subset of the actual measurements.

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That Changed Everything…..
Extract from abstract
Results were published in 1993
“On the Self-Similar Nature of Ethernet Traffic”
Will E. Leland, Walter Willinger, Daniel V. Wilson, Murad S. Taqqu
“We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, that such behavior has serious implications for the design, control, and analysis of high-speed…”
Source: Prof. V. Mishra, Columbia U. (edited)

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Fractals
Source: Prof. P. Barford, U. Wisconsin

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Traffic at different time scales
(Bellcore traces)
bursty
still bursty
Source: Prof. P. Barford (edited)

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Source: Prof. V. Mishra, Columbia U.

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What is the observation?
A Poisson process
When observed on a fine time scale will appear bursty
When aggregated on a coarse time scale will flatten (smooth) to white noise
A Self-Similar (fractal) process
When aggregated over wide range of time scales will maintain its bursty characteristic
Source: Prof. C. Williamson

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Why do we care?
For traffic with the same average, the probability of a buffer overflow of self-similar traffic is much higher than with Poisson traffic
Costs of buffers (memory) are 1/3 the cost of a high-speed router !
When aggregating traffic from multiple sources, self-similar traffic becomes burstier, while Poisson traffic becomes smoother