Speaker: Yu-Fu HuangAdvisor: Dr. Kai-Wei KeDate : 2014, Mar. 17

A page-oriented WWW traffic model for wireless system simulations

Outline• Traffic models from Poisson to Self-Similar•WWW Traffic structure •Web traffic characterization• Simulation and results•Conclusion•Reference

The interest towards traffic model• Traffic models are needed as input in network simulation.•A good traffic model may lead to a better understanding of the characteristics of the network traffic itself.

Stochastic Counting Process• Poisson process ⊆ Renewal process• Independent increment•Memoryless property• Inter-arrival time pdf: Exponential

•Renewal process• Independent increment• Inter-arrival time pdf: Arbitrary

X1 X2 X3 X4 X5 X6 X7

T=X1+X2+X3+X4+…X1,X2,X3… are i.i.dPoisson process: - Any point in the time axis meets Memoryless property.Renewal process: - Only point exactly at exiting one period and entering a new period meets Memoryless property.

t

Variance of sample mean approaches to zero as n approaches to infinite.

Traffic models from Poisson to Self-Similar• Self-Similar process• Long Range Dependency• Infinite Variance

Heavy-tailed probability distribution

Outline• Traffic models from Poisson to Self-Similar•WWW Traffic structure •Web traffic characterization• Simulation and results•Conclusion•Reference

WWW Traffic structure• Two approaches to data traffic modelling:• Behaviorist or black-box approach:• Modelled w/o taking into account the causes that lead to them

• Structure approach:• Model design is based on the internal structure of traffic generating system

Outline• Traffic models from Poisson to Self-Similar•WWW Traffic structure •Web traffic characterization• Simulation and results•Conclusion•Reference

Pages per session

Time between pages

Page size

Heavy-tailed probability distribution

Packet size

Packet inter-arrival time

Page

Packet

PIT

Outline• Traffic models from Poisson to Self-Similar•WWW Traffic structure •Web traffic characterization• Simulation and results•Conclusion•Reference

Test conditions4MB Queue

2000s of average session interarrival time

Constant service rate of 2 KBps

82.75s of average session interarrival time

(I)(II)

Test condition (I): With proposed model adapted to corporate environment Server utilization rate: 68% With ETSI model adapted to corporate environment Server utilization rate: 3%Test condition (II): With proposed model adapted to corporate environment Server utilization rate: 68% With ETSI model adapted to corporate environment but increasing average session interarrival time from 2000s to 82.75s Get server utilization rate: 68%

Adjusted Utilization ESTI

model

ESTI ModelParameter Distribution Main value

Session interarrival time ExponentialPages per session(Packet calls)

Geometrical Mean = 5 p.p.s

Time between pages(Reading time)

Geometrical Mean = 412s

Page size Geometrical Mean = 25 packets

Packet size Min. Paretoα = 1.1min. k = 81.5 bytesmax. m = 66666 bytes

Packet interarrival time Geometrical Mean = 0.125s

Test condition (I)

Test condition (II)

Conclusions (I)• Traffic models summary:• Independent interarrival time: Exponential• Session or packet interarrival

•Cumulative independent interarrival time: Gamma or Erlang distribution• Page interarrival

•Data size: Self-similar distribution• Page size

Conclusions (II)•ESTI model underestimates packet losses and delay in a queue due to the low load offered by the ESTI model.• The proposed model generates a traffic load similar to the measured one and much more burstiness than the ESTI one.

Reference[2] Staehle D., Leibnitz K., and Tran-Gia P., “Source Traffic Modeling of Wireless Application” Institut für Informatik, Würzburg Universität, Technical Report No. 261, June 2000.

[1] Reyes-Lecuona A., González-Parada E., and Díaz-Estrella A., “A page-oriented WWW traffic model for wireless system simulations” Proceedings of the 16th International Teletraffic Congress (ITC16), Edinburgh, United Kindom, pp. 1271-1280, June 1999.

[3] Michela Becchi, “From Poisson Process to Self-Similarity: a Survey of Network Traffic Models” mbecchi@wustl.edu.