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pathChirp Efficient Available Bandwidth Estimation

pathChirp Efficient Available Bandwidth Estimation. Vinay Ribeiro Rice University Rolf Riedi Jiri Navratil Rich Baraniuk Les Cottrell (Rice) (SLAC). Network Model. Packet delay = constant term (propagation, service time)

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pathChirp Efficient Available Bandwidth Estimation

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  1. pathChirpEfficient Available Bandwidth Estimation Vinay Ribeiro Rice University Rolf Riedi Jiri Navratil Rich Baraniuk Les Cottrell (Rice) (SLAC)

  2. Network Model Packet delay = constant term (propagation, service time) + variable term (queuing delay) • End-to-end paths • Multi-hop • No packet reordering • Router queues • FIFO • Constant service rate

  3. Available Bandwidth • Unused capacity along path Available bandwidth: • Goal: use end-to-end probing to estimate available bandwidth

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

  5. 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

  6. 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

  7. 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]

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

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

  10. Chirps vs. Packet-Pairs • Each chirp train of N packets contains N-1 packet pairs at different spacings • Reduces load by 50% • Chirps: N-1 packet spacings, N packets • Packet-pairs: N-1 packet spacings, 2N-2 packets • Captures temporal queuing behavior

  11. Chirps vs. CBR Trains • Multiple rates in each chirping train • Allows one estimate per-chirp • Potentially more efficient estimation

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

  13. Bursty Cross-Traffic Scenario • Goal: exploit information in queuing delay signature

  14. PathChirp Methodology • Per-packet pair available bandwidth, (k=packet number) • Per-chirp available bandwidth • Smooth per-chirp estimate over sliding time window of size

  15. Self-Induced Congestion Heuristic • Definitions: delay of packet k inst rate at packet k

  16. Excursions • Must take care while using self-induced congestion principle • Segment signature into excursions from x-axis • Valid excursions are those consisting of at least “L”packets • Apply only to validexcursions

  17. Valid excursion increasing queuing delay • Valid excursion decreasing queuing delay • Invalid excursions • Last excursion Setting Per-Packet Pair Available Bandwidth

  18. 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

  19. Performance with Varying Parameters • Vary probe size, spread factor • Probing load const. • Mean squared error (MSE) of estimates Result: MSE decreases with increasing probe size, decreasing spread factor

  20. Multi-hop Experiments • First queue is bottleneck • Compare • No cross-traffic at queue 2 • With cross-traffic at queue 2 • Result: MSE close in both scenarios

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

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

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

  24. Conclusions • Chirp trains • Probe at multiple rates simultaneously • Efficient estimates • pathChirp • Self-induced congestion • Excursion detection • Experiments • Internet experiments promising • Large probe packet size, small spread factor better • Outperforms existing tools • open-source code is available at spin.rice.edu • Demo during 10:30a.m. break

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