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Multiscale Traffic Processing Techniques for Network Inference and Control

Multiscale Traffic Processing Techniques for Network Inference and Control. Richard Baraniuk Edward Knightly Robert Nowak Rolf Riedi Rice University INCITE Project September 2000. INCITE. I nter N et C ontrol and I nference T ools at the E dge.

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Multiscale Traffic Processing Techniques for Network Inference and Control

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  1. Multiscale Traffic Processing Techniques for Network Inference and Control Richard Baraniuk Edward Knightly Robert Nowak Rolf Riedi Rice University INCITE Project September 2000

  2. INCITE InterNet Control and Inference Tools at the Edge • Overall Objective:Scalable,edge-based tools for on-line network analysis, modeling, and measurement • Theme for DARPA NMS Research:Multiscale traffic analysis, modeling, and processing via multifractals • Expertise:Statistical signal processing, mathematics, network QoS Rice University | INCITE Project | September 2000

  3. Technical Challenges • Poor understanding of origins of complex network dynamics • Lack of adequate modeling techniques for network dynamics • Internal network inaccessible Need:Manageable, reduced-complexity models with characterizable accuracy Rice University | INCITE Project | September 2000

  4. Innovative Tools - 1 • Multifractals: a natural “language” and toolset for traffic with • bursts and high variability on multiple time scales • short-range dependencies (SRD) • long-range dependencies (LRD) _________ _________ Rice University | INCITE Project | September 2000

  5. Innovative Tools - 2 • Reduced-complexity statistical traffic models based on multifractal trees and cascades • realistic capture multiscale variability, SRD+LRD, non-Gaussianity • analytically tractable eg: queuing analysis • linear complexity algorithmsO(N) • Statistical inference tools for model fitting, end-to-end path modeling Rice University | INCITE Project | September 2000

  6. Multiscale Traffic Modeling Time Scale Multiplicative innovations Rice University | INCITE Project | September 2000

  7. Multifractal Wavelet Model (MWM) • Random multiplicativeinnovationsAj,k on [0,1]eg: beta • Parsimonious modeling(one parameter per scale) • Strong ties with rich theory of multifractals Rice University | INCITE Project | September 2000

  8. Multiscale Traffic Trace Matching Auckland 2000 MWM match scale 4ms 16ms 64ms Rice University | INCITE Project | September 2000

  9. Marginal Matching Auckland 2000 MWM Gaussian scale 4ms 16ms 64ms Rice University | INCITE Project | September 2000

  10. Multiscale Queuing Rice University | INCITE Project | September 2000

  11. End-to-End Path Modeling • Abstract network dynamics into a single bottleneck queue driven by “effective cross-traffic” • Goal: Estimate volume of cross-traffic Rice University | INCITE Project | September 2000

  12. Probing • Ideally: delay spread of packet pair spaced by T sec correlates with cross-traffic volume at time-scale T Rice University | INCITE Project | September 2000

  13. Probing Uncertainty Principle • Should not allow queue to empty between probe packets • Small T for accurate measurements • but probe traffic would disturb cross-traffic (and overflow bottleneck buffer!) • Larger T leads to measurement uncertainties • queue could empty between probes • To the rescue: model-based inference Rice University | INCITE Project | September 2000

  14. Multifractal Cross-Traffic Inference • Model bursty cross-traffic using MWM Rice University | INCITE Project | September 2000

  15. Efficient Probing: Packet Chirps • MWM tree inspires geometric chirp probe • MLE estimates of cross-traffic at multiple scales Rice University | INCITE Project | September 2000

  16. Chirp Probe Cross-Traffic Inference Rice University | INCITE Project | September 2000

  17. ns-2 Simulation • Inference improves with increased utilization Low utilization (39%) High utilization (65%) Rice University | INCITE Project | September 2000

  18. ns-2 Simulation (Adaptivity) • Inference improves as MWM parameters adapt MWM parameters Inferred x-traffic Rice University | INCITE Project | September 2000

  19. Adaptivity (MWM Cross-Traffic) Rice University | INCITE Project | September 2000

  20. Challenges: Path Modeling • Packet chirps balance measurement accuracy vs. disturbance to network and cross-traffic • Enhancements needed: • rigorous statistical accuracy analysis • multiple bottleneck queues • passive monitoring • deal with losses as well as delays • closed loop paths (feedback) • practical implementation issues(clock jitter, estimating bottleneck service rate, ...) • Verification with real Internet experiments(need “ground truth” info on cross-traffic) Rice University | INCITE Project | September 2000

  21. INCITE: Near-term Goals • Multifractal analysis, modeling, synthesis toolbox • Path modelingtheory and toolbox • Preliminary verification • simulations (ns-2) • Rice testbed • Enron, Nokia, Texas Instruments • IPEX / XIWT Rice University | INCITE Project | September 2000

  22. INCITE: Longer-Term Goals • New traffic models, inference algorithms • theory, simulation, real implementation • Applications to control, QoS, network meltdown early warning • TI Avalanche measurement system • Leverage from our other projects • ATR program (DARPA, ONR, ARO) • RENE • NSF ITR Rice University | INCITE Project | September 2000

  23. Rice ATR Project • Modeling, compression, automatic target recognition of multi-modal images, maps, …D. Healy (DARPA), W. Masters, W. Martinez (ONR), W. Sander (ARO) Rice University | INCITE Project | September 2000

  24. Leverage from Other Rice Projects • RENE (NSF, Nokia, TI) • large wireless networking project (6 PIs) • substantial traffic modeling component • ITR/INDRA (NSF SPN, ITR) • $5M collaboration between Rice/CMU/Virginia/Berkeley • scalable services: QoS communication, multicast and mirroring/caching • three core services: transfer, replication, and storage Rice University | INCITE Project | September 2000

  25. Natural Synergies • Modeling team: New insights into • key traffic features models should capture • origins of complex network dynamics • Simulation team • fast synthesis of realistic aggregate traffic • Measurement team • novel model-based inference schemes • what and where to measure • Emulation team • level of detail for desired realism • Design • “what if?” • new approaches to control Rice University | INCITE Project | September 2000

  26. Natural Synergies • What we need: • critique of our models • insight into the physical network mechanisms to inspire new modeling simplificationseg: how many bottlenecks on a typical path? • discussions on practical implementation issues • verification experiments (“ground truth”) (scale up from ns and Rice testbed) Rice University | INCITE Project | September 2000

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