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Multiplicative Wavelet Traffic Model and pathChirp : Efficient Available Bandwidth Estimation

This research paper presents a multiplicative wavelet traffic model for accurate traffic estimation and prediction. Additionally, the pathChirp tool is introduced for efficient available bandwidth estimation in network paths.

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Multiplicative Wavelet Traffic Model and pathChirp : Efficient Available Bandwidth Estimation

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  1. Multiplicative Wavelet Traffic ModelandpathChirp: Efficient Available Bandwidth Estimation Vinay Ribeiro

  2. The Internet • Congestion key problem

  3. Network Traffic Modeling • Traffic = packet arrival process on a link • Traffic is bursty • Bursts can cause buffer overflows • Need accurate traffic models for • Simulation, estimation, prediction, control

  4. Multiscale Aggregation Analysis of Traffic time unit 4 ms 2 ms 1 ms

  5. Failure of Classical Models Internet Traffic Classical Traffic Model time unit 600 ms 60 ms 6 ms Internet traffic is self-similar: looks similar at different time scales

  6. Why Self-similarity is Important • Self-similarity leads to larger queues • Classical models are overly optimistic

  7. Multiscale Tree Structure time unit 4 ms 2 ms 1 ms

  8. Additive Traffic Model Coarse-to-fine multiscale synthesis • Generate additive innovations, W • Match variance at each level in tree • FastO(N) algorithm

  9. Additive Model Sample Realization Iteration/scale 0 1 2 3 8 11

  10. Limitations of Additive Models • Addition  Gaussian process • Gaussian, takes negative values • Gaussian not spiky • Goal: model that gives positive and spiky data

  11. Multiplicative Traffic Model Coarse-to-fine multiscale synthesis • Generate independent positive multiplicative innovations, • FastO(N) synthesis algorithm

  12. Multiplicative Model Realization Iteration/scale 0 1 2 3 8 11

  13. Time Series Comparison of Models time unit 24 ms 12 ms 6 ms Berkeley data Multiplicative model Additive model

  14. Histogram Comparison of Models time unit 24 ms 12 ms 6 ms Berkeley data Multiplicative model Additive model

  15. Queuing Experiments • Study queue overflow probability P(Q>b)

  16. Queuing Results • Plot log P(Q>b) vs. b • Additive model underestimates losses (congestion) Berkeley traffic Multiplicative model Additive model

  17. Advantages of Multiplicative Model • Synthesized traffic • Positive • Spiky • Self-similar • Algorithm • FastO(N) synthesis • Queuing • Outperforms additive model • Uses • Simulation, estimation, congestion control, prediction

  18. From Links to Paths • Inferring path properties useful for many applications

  19. pathChirpEfficient Available Bandwidth Estimation

  20. Available Bandwidth • Unused capacity along path Available bandwidth: • Goal: estimate available bandwidth from probe packet transfer delays • Delay=speed of light propagation + queuing delay

  21. Applications • Server selection • Route selection (e.g. BGP) • Network monitoring • SLA verification • Congestion control

  22. Available Bandwidth Probing Tool Requirements • Fast estimate within few RTTs • Unobtrusive introduce light probing load • Accurate • No topology information(e.g. link speeds) • Robustto multiple congested links • No topology information(e.g. link speeds) • Robustto multiple congested links

  23. Principle of Self-Induced Congestion • Advantages • No topology information required • Robust to multiple bottlenecks • TCP-Vegas uses self-induced congestion principle Probing rate < available bw  no delay increase Probing rate > available bw  delay increases

  24. Vary sender packet-pair spacing • Compute avg. receiver packet-pair spacing • Constrained regression based estimate • Shortcoming: packet-pairs • do not capture temporal • queuing behavior useful for • available bandwidth • estimation Packet-pairs Packet train Trains of Packet-Pairs (TOPP)[Melander et al]

  25. Pathload [Jain & Dovrolis] • Constant bit rate (CBR) packet trains • Vary rate of successive trains • Converge to available bandwidth • Shortcoming • Efficiency: only one data rate per train

  26. Chirp Packet Trains • Exponentiallydecrease packet spacing withinpacket train • Wide range of probing rates • Efficient:few packets

  27. CBR Cross-Traffic Scenario • Point of onset of increase in queuing delay gives available bandwidth

  28. Bursty Cross-Traffic Scenario • Goal: exploit information in queuing delay signature • Use principle of self-induced congestion

  29. pathChirp Tool • UDPprobe packets • No clock synchronization required, only uses relative queuing delay within a chirp duration • Computation at receiver • Context switching detection • User specified average probing rate • open source distribution at spin.rice.edu

  30. Internet Experiments • 3 common hops between SLACRice and ChicagoRice paths • Estimates fall in proportion to introduced Poisson traffic

  31. Comparison with TOPP • Equal avg. probing rates for pathChirp and TOPP • Result: pathChirp outperforms TOPP 30% utilization 70% utilization

  32. Comparison with Pathload • 100Mbps links • pathChirp uses 10 times fewer bytes for comparable accuracy

  33. Summary • Multiplicative wavelet model for traffic • Positive and spiky data • Outperforms additive Gaussian models • Freeware code: dsp.rice.edu • pathChirp • Special chirp packet trains • Efficient available bandwidth estimation • Freeware code: spin.rice.edu

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