01-06-2012, 12:16 PM
Network Traffic Characteristics
[attachment=23825]
Motivation for Network Traffic Study
Understanding network traffic behavior is essential for all aspects of network design and operation
Component design
Protocol design
Provisioning
Management
Modeling and simulation
The Story Begins with Measurement
In 1989, Leland and Wilson begin taking high resolution traffic traces at Bellcore
Ethernet traffic from a large research lab
100 m sec time stamps
Packet length, status, 60 bytes of data
Mostly IP traffic (a little NFS)
Four data sets over three year period
Over 100m packets in traces
Traces considered representative of normal use
The packet count picture tells all
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
Self-similarity: manifestations
Self-similarity manifests itself in several equivalent fashions:
Slowly decaying variance
Long range dependence
Non-degenerate autocorrelations
Hurst effect
Graphical Tests for Self-Similarity
Variance-time plots
Relies on slowly decaying variance of self-similar series
The variance of X(m) is plotted versus m on log-log plot
Slope (-b) greater than –1 is indicative of SS
R/S plots
Relies on rescaled range (R/S)statistic growing like a power law with H as a function of number of points n plotted.
The plot of R/S versus n on log-log has slope which estimates H
Periodogram plot
Relies on the slope of the power spectrum of the series as frequency approaches zero
The periodogram slope is a straight line with slope b – 1 close to the origin
[attachment=23825]
Motivation for Network Traffic Study
Understanding network traffic behavior is essential for all aspects of network design and operation
Component design
Protocol design
Provisioning
Management
Modeling and simulation
The Story Begins with Measurement
In 1989, Leland and Wilson begin taking high resolution traffic traces at Bellcore
Ethernet traffic from a large research lab
100 m sec time stamps
Packet length, status, 60 bytes of data
Mostly IP traffic (a little NFS)
Four data sets over three year period
Over 100m packets in traces
Traces considered representative of normal use
The packet count picture tells all
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
Self-similarity: manifestations
Self-similarity manifests itself in several equivalent fashions:
Slowly decaying variance
Long range dependence
Non-degenerate autocorrelations
Hurst effect
Graphical Tests for Self-Similarity
Variance-time plots
Relies on slowly decaying variance of self-similar series
The variance of X(m) is plotted versus m on log-log plot
Slope (-b) greater than –1 is indicative of SS
R/S plots
Relies on rescaled range (R/S)statistic growing like a power law with H as a function of number of points n plotted.
The plot of R/S versus n on log-log has slope which estimates H
Periodogram plot
Relies on the slope of the power spectrum of the series as frequency approaches zero
The periodogram slope is a straight line with slope b – 1 close to the origin