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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 April 2001. 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 April 2001

  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.rice.edu | April 2001

  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.rice.edu | April 2001

  4. Multiscale modeling Rice University | INCITE.rice.edu | April 2001

  5. Multiscale Analysis Time Multiscale statistics Var1 Scale Var2 Var3 Analysis: flow up the tree by adding Varj Start at bottom with trace itself Rice University | INCITE.rice.edu | April 2001

  6. Multiscale Synthesis Time Start at top with total arrival Multiscale parameters Var1 Scale Var2 Var3 Synthesis: flow down via innovations Varj Signal: bottom nodes Rice University | INCITE.rice.edu | April 2001

  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.rice.edu | April 2001

  8. Multiscale Traffic Trace Matching Auckland 2000 MWM match scale 4ms 16ms 64ms Rice University | INCITE.rice.edu | April 2001

  9. Multiscale Queuing Rice University | INCITE.rice.edu | April 2001

  10. Probing the Network Rice University | INCITE.rice.edu | April 2001

  11. Probing • Ideally: delay spread of packet pair spaced by T sec correlates with cross-traffic volume at time-scale T Rice University | INCITE.rice.edu | April 2001

  12. 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.rice.edu | April 2001

  13. Multifractal Cross-Traffic Inference • Model bursty cross-traffic using MWM Rice University | INCITE.rice.edu | April 2001

  14. Efficient Probing: Packet Chirps • MWM tree inspires geometric chirp probe • MLE estimates of cross-traffic at multiple scales Rice University | INCITE.rice.edu | April 2001

  15. Chirp Probe Cross-Traffic Inference Rice University | INCITE.rice.edu | April 2001

  16. ns-2 Simulation • Inference improves with increased utilization Low utilization (39%) High utilization (65%) Rice University | INCITE.rice.edu | April 2001

  17. ns-2 Simulation (Adaptivity) • Inference improves as MWM parameters adapt MWM parameters Inferred x-traffic Rice University | INCITE.rice.edu | April 2001

  18. Adaptivity (MWM Cross-Traffic) Eg: Route changes Rice University | INCITE.rice.edu | April 2001

  19. Comparing Probing schemes Rice University | INCITE.rice.edu | April 2001

  20. Comparing probing schemes • `Classical’: Bandwidth estimation by packet pairs and trains • Novel: Traffic estimation, probing best by Uniform? Poisson? Chirp? Rice University | INCITE.rice.edu | April 2001

  21. Model based Probing Chirp: model based, superior Uniform: Uncertainty increases error Rice University | INCITE.rice.edu | April 2001

  22. Impact of Probing on Performance Heavy Heavy probing - reduces bandwidth - increases loss - inflicts time-outs NS-simulation: Same `web-traffic’ with variable probing rates Light Rice University | INCITE.rice.edu | April 2001

  23. Influence of probing rate on error • Chirp probing performing uniformly good • Uniform requires higher rates to perform Rice University | INCITE.rice.edu | April 2001

  24. Synergies • SAIC (Warren): MWM code for real time simulator • SLAC (Cottrell, Feng): Modify PingER for chirp-probing High performance networks • Demo: C-code for real world chirp-probing using NetDyn (TCP) + simple Daemon at receiver (INRIA France, UFMG Brazil, Michigan State) Rice University | INCITE.rice.edu | April 2001

  25. INCITE: Near-term / Ongoing • Verification with real Internet experiments • Rice testbed (practical issues) • SAIC (real time algorithms) • SLAC / ESNet (real world verification) • Enhancements: • rigorous statistical error analysis • deal with random losses • multiple bottleneck queues (see demo) • passive monitoring (novel models) • closed loop paths/feedback (ns-simulation) Rice University | INCITE.rice.edu | April 2001

  26. INCITE: Longer-Term Goals • New traffic models, inference algorithms • theory, simulation, real implementation • Applications to Control, QoS, Network Meltdown early warning • Leverage from our other projects • ATR program (DARPA, ONR, ARO) • RENE (Rice Everywhere Network:NSF) • NSF ITR • DoE Rice University | INCITE.rice.edu | April 2001

  27. Stationary multifractals Rice University | INCITE.rice.edu | April 2001

  28. Stationary multiplicative models • j(s): stationary, indep., E[j(s)]=1 • A(t) = lim 0t 1(s) 2(s)… n(s) ds • May degenerate (compare: MWM is conservative) • stationary increments • Assume j(2j s) are i.i.d.; Renewal reward • Compare MWM: j(2j s) constant over [k,k+1] • If Var()<1: Convergence in L2 ; E[A(t)]=t • Multifractal function: T(q)=q-log2E[q] Rice University | INCITE.rice.edu | April 2001

  29. Simulation • L2 criterion for convergence translates to T(2)>0 • Conjecture: For q>1 converge in Lq if T(q)>0 Thus non-degenerate iff T’(1)>0, ie E[ L log (L /2) ] >0 Rice University | INCITE.rice.edu | April 2001

  30. Parameter estimation • No conservation: can’t isolate multipliers • Possible correlation within multipliers • IID values: • Z(s) = log [ 1(s) 2(s)… n(s) ] • Cov(Z(t)Z(t+s))= Si=1..n exp(-lis)Var i(s) • `LRD-scaling’ at medium scales, but SRD. Multifractal subordination -> true LRD. Rice University | INCITE.rice.edu | April 2001

  31. INCITE.rice.edu Rice University | INCITE.rice.edu | April 2001

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