2 Something “feels the same” regardless of scale 4 What is that???

Will E. Leland, Walter Willinger and Daniel V. Wilson BELLCOREMurad S. Taqqu Boston University

CS634 ADVANCED COMPUTER NETWORKING

COMPUTER SCIENCE

COLLEGE OF WILLIAM AND MARY

ON THE SELF-SIMILAR NATURE OF

ETHERNET TRAFFIC

Presented by: Feng Yan

OVERVIEW

What is Self Similarity?

Ethernet Traffic is Self-Similar

Implications of Self Similarity

Conclusion

Discussion2

PART 1:

WHAT IS SELF-SIMILARITY ?

INTUITION OF SELF-SIMILARITY

Something “feels the same” regardless of scale

4What is that???



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Self-similar in nature



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The Koch snowflake fractal



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Categories:

Exact self-similarity: Strongest Type

Approximate self-similarity: Loose Form

Statistical self-similarity: Weakest Type


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Approximate self-similarity:

Recognisably similar but not exactly so.

e.g. Mandelbrot set

Statistical self-similarity:

Only numerical or statistical measures that are preserved

across scales

STOCHASTIC OBJECTS

In case of Stochastic Objects

e.g. time-series

Self-similarity is used in the distributional sense

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WHY SELF-SIMILARITY IMPORTANT?

Recently, network packet traffic has been identified as being self-similar.

Current network traffic modeling using Poisson distributing (etc.) does not take into account the self-similar nature of traffic.

This leads to inaccurate modeling of network traffic. 13

PROBLEMS WITH CURRENT MODELS

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

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SELF-SIMILARITY BY PICTURE

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packets per time unit

Ethernet traffic August’89 trace

CURRENT MODELING BY PICTURE

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SIDE-BY-SIDE VIEW

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COMPARE BY VIEW

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CONSEQUENCES OF SELF-SIMILARITY

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Bursty Data

Streams

Aggregation

Smooth Pattern

Streams

Bursty Data

Streams

Aggregation

Bursty Aggregate

Streams

Reality (self-similar):

Current Model:

Consequence: Inaccuracy

MATHEMATICAL DEFINITIONS

Long-range Dependence autocorrelation decays slowly

Hurst Parameter Developed by Harold Hurst (1965) H is a measure of “burstiness”

▪ also considered a measure of self-similarity 0 < H < 1 H increases as traffic increases

▪ i.e., traffic becomes more self-similar20

PROPERTIES OF SELF SIMILARITY

X = (Xt : t = 0, 1, 2, ….) is covariance stationary random process (i.e. Cov(Xt,Xt+k) does not depend on t for all k)

Let X(m)={Xk(m)} denote the new process obtained by

averaging the original series X in non-overlapping sub-blocks of size m.

Mean , variance 2

Suppose that Autocorrelation Function r(k) k -β, 0<β<1

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e.g. X(1)= 4,12,34,2,-6,18,21,35

Then X(2)=8,18,6,28

X(4)=13,17

DEFINITION BY AUTO-CORRELATION

X is exactly second-order self-similar if The aggregated processes have the same

autocorrelation structure as X. i.e. r (m) (k) = r(k), k0 for all m =1,2, …

X is asymptotically second-order self-similar ifthe above holds when [ r (m) (k) r(k), m ]

Most striking feature of self-similarity: Correlation structures of the aggregated process do not degenerate as m 22

23lag

ACF


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TRADITIONAL MODELS

Correlation structures of their aggregated processes degenerate as m i.e. r (m) (k) 0 as m , for k = 1,2,3,...

Short Range Dependence Processes: Exponential Decay of autocorrelations i.e. r(k) ~ pk , as k , 0 < p < 1 Summation is finite

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LONG RANGE DEPENDENCE

Processes with Long Range Dependence are characterized by an autocorrelation function that decays hyperbolically as k increases

Important Property: This is also called non-summability of correlation

kkr )(

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INTUITION

The intuition behind long-range dependence:

While high-lag correlations are all individually small, their cumulative affect is important

Gives rise to features drastically different from conventional short-range dependent processes

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THE MEASURE OF SELF-SIMILARITY

Hurst Parameter H , 0.5 < H < 1

Three approaches to estimate H (Based on properties of self-similar processes) Variance Analysis of aggregated

processes Rescaled Range (R/S) Analysis for

different block sizes: time domain analysis

Periodogram Analysis: frequency domain analysis (Whittle Estimator)

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!

VARIANCE ANALYSIS

Variance of aggregated processes decays as: Var(X(m)) = am-b as m infinite,

For short range dependent processes (e.g. Poisson Process):

Var(X(m)) = am-1 as m infinite,

Plot Var(X(m)) against m on a log-log plot

Slope > -1 indicative of self-similarity29

VARIANCE PLOT EXAMPLE

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Slope=-1

Slope=-0.7

THE R/S STATISTIC

)],......,,0min(),......,,0[max()(

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where

For a given set of observations,

Rescaled Adjusted Range or R/S statistic is given by

EXAMPLE

Xk = 14,1,3,5,10,3

Mean = 36/6 = 6W1 =14-(1*6 )=8W2 =15-(2*6 )=3W3 =18-(3*6 )=0W4 =23-(4*6 )=-1W5 =33-(5*6 )=3W6 =36-(6*6 )=0 32

R/S = 1/S*[8-(-1)] = 9/S

THE HURST EFFECT

For self-similar data, rescaled range or R/S statistic grows according to cnH H = Hurst Paramater, > 0.5

For short-range processes , R/S statistic ~ dn0.5

History: The Nile river In the 1940-50’s, Harold Edwin Hurst studied the 800-year

record of flooding along the Nile river. (yearly minimum water level) Finds long-range dependence.

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POX PLOT EXAMPLE

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Slope = 1.0

Slope = 0.5

Slope = 0.79

WHITTLE ESTIMATOR

Provides a confidence interval

Property: Any long range dependent process approaches fractional Gaussian noise (FGN), when aggregated to a certain level

Test the aggregated observations to ensure that it has converged to the normal distribution 35

SUMMARY

Self-similarity manifests itself in several equivalent fashions:

Non-degenerate autocorrelations Slowly decaying variance Long range dependence Hurst effect

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PART 2:

ETHERNET TRAFFIC IS SELF-SIMILAR

THE FAMOUS DATA

Leland and Wilson collected hundreds of millions of Ethernet packets without loss and with recorded time-stamps accurate to within 100µs.

Data collected from several Ethernet LAN’s at the Bellcore Morristown Research and Engineering Center at different times over the course of approximately 4 years.

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PLOTS SHOWING SELF-SIMILARITY (Ⅰ)

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H=0.5

H=0.5

H=1

Estimate H 0.8

PLOTS SHOWING SELF-SIMILARITY (Ⅱ)

41Higher Traffic, Higher H

High Traffic

Mid Traffic

Low Traffic

1.3%-10.4%

3.4%-18.4%

5.0%-30.7%

Packets

H : A FUNCTION OF NETWORK UTILIZATION

Observation shows “contrary to Poisson”

Network Utilization H

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As number of Ethernet users increases, the resulting aggregate traffic becomes burstier instead of smoother

DIFFERENCE IN LOW TRAFFIC H VALUES

Pre-1990: host-to-host workgroup traffic

Post-1990: Router-to-router traffic

Low period router-to-router traffic consists mostly of machine-generated packets Tend to form a smoother arrival stream,

than low period host-to-host traffic43

SUMMARY

Ethernet LAN traffic is statistically self-similar

H : the degree of self-similarityH : a function of utilizationH : a measure of “burstiness”

Models like Poisson are not able to capture self-similarity

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PART 3:

IMPACT OF SELF SIMILARITY

COMPARISON

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TWO EFFECTS

The superposition of many ON/OFF sources whose ON-periods and OFF-periods exhibit the Noah Effect produces aggregate network traffic that features the Joseph Effect.

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Also known as packet train models

Noah Effect: high variability or infinite variance

Joseph Effect: Self-similar or

long-range dependent traffic

EXISTING MODELS

Traditional traffic models: finite variance ON/OFF source models

Superposition of such sourcesbehaves like white noise, with only short range correlations

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EASY MODELING: NOAH EFFECT

Questions related to self-similarity can be reduced to practical implications of Noah Effect

Queuing and Network performance Network Congestion Controls Protocol Analysis

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The Queue Length distribution Traditional (Markovian) traffic: decreases exponentially

fast Self-similar traffic: decreases much more slowly

Not accounting for Joseph Effect can lead to overly optimistic performance

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Effect of H (Burstiness)

QUEUING PERFORMANCE

CONGESTION CONTROL

How to design the buffer size?

Trade-off between Packet Lose and Packet Delay

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Packet Lose Packet Delay

Short Range Dependence

Decrease Exponentially

Fixed Limit

Long Range Dependence

Decrease Slowly Always Increase

Compare SRD and LRD when increase buffer size

CONGESTION CONTROL

PROTOCOL DESIGN

Protocol design should take into account knowledge about network traffic such as the presence or absence of the self-similarity.

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Parsimonious Models Small number of parameters Every parameter has a physically meaningful

interpretation e.g. Mean , Variance 2, H Doesn’t quantify the effects of various factors in traffic

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CONCLUSION

Demonstrated the existence of self-similarity in Ethernet Traffic irrespective of time scales

Proposed the degree of self-similarity can be measured by Hurst parameter H (higher H implies burstier traffic)

Illustrated the difference between the self-similar and standard models

Explained Importance of self similarity in design, control, performance analysis

A USEFUL LINK AND MATERIALS

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http://ita.ee.lbl.gov/html/contrib/BC.html

http://ita.ee.lbl.gov/html/contrib/BC.html

Questions?

THANK YOU!

2 Something “feels the same” regardless of scale 4 What is that???

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