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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. - PowerPoint PPT Presentation
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Multiscale Traffic Processing Techniques for Network Inference and Control
Richard Baraniuk Edward Knightly Robert Nowak Rolf Riedi
Rice University INCITE ProjectApril 2001
Rice University | INCITE.rice.edu | April 2001
INCITEInterNet 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
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
Multiscale modeling
Rice University | INCITE.rice.edu | April 2001
Multiscale Analysis
Time
Scale
Analysis: flow up the tree by adding
Start at bottom with trace itself
Var1
Var2
Var3
Varj
Multiscale statistics
Rice University | INCITE.rice.edu | April 2001
Multiscale Synthesis
Time
Scale
Synthesis: flow down via innovations
Start at top with total arrival
Signal: bottom nodes
Var1
Var2
Var3
Varj
Multiscale parameters
Rice University | INCITE.rice.edu | April 2001
Multifractal Wavelet Model (MWM)
• Random multiplicativeinnovations Aj,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
Multiscale Traffic Trace Matching
4ms
16ms
64ms
Auckland 2000 MWM matchscale
Rice University | INCITE.rice.edu | April 2001
Multiscale Queuing
Rice University | INCITE.rice.edu | April 2001
Probing the Network
Rice University | INCITE.rice.edu | April 2001
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
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
Multifractal Cross-Traffic Inference
• Model bursty cross-traffic using MWM
Rice University | INCITE.rice.edu | April 2001
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
Chirp Probe Cross-Traffic Inference
Rice University | INCITE.rice.edu | April 2001
ns-2 Simulation
• Inference improves with increased utilization
Low utilization (39%) High utilization (65%)
Rice University | INCITE.rice.edu | April 2001
ns-2 Simulation (Adaptivity)
• Inference improves as MWM parameters adapt
MWM parameters Inferred x-traffic
Rice University | INCITE.rice.edu | April 2001
Adaptivity (MWM Cross-Traffic)
Eg: Route changes
Rice University | INCITE.rice.edu | April 2001
Comparing Probing schemes
Rice University | INCITE.rice.edu | April 2001
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
Model based Probing
Chirp: model based, superior
Uniform: Uncertainty increases error
Rice University | INCITE.rice.edu | April 2001
Impact of Probing on Performance
Heavy probing - reduces bandwidth - increases loss - inflicts time-outs
NS-simulation: Same `web-traffic’ with variable probing rates
Heavy
Light
Rice University | INCITE.rice.edu | April 2001
Influence of probing rate on error
• Chirp probing performing uniformly good• Uniform requires higher rates to perform
Rice University | INCITE.rice.edu | April 2001
Synergies
• SAIC (Warren): MWM code for real time simulator
• SLAC (Cottrell, Feng):Modify PingER for chirp-probingHigh performance networks
• Demo: C-code for real world chirp-probingusing NetDyn (TCP) + simple Daemon at receiver(INRIA France, UFMG Brazil, Michigan State)
Rice University | INCITE.rice.edu | April 2001
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
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
Stationary multifractals
Rice University | INCITE.rice.edu | April 2001
Stationary multiplicative models
j(s): stationary, indep., E[j(s)]=1
• A(t) = lim 0t 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
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[ log ( /2) ] >0
Rice University | INCITE.rice.edu | April 2001
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))= i=1..n exp(-is)Var i(s)
• `LRD-scaling’ at medium scales, but SRD. Multifractal subordination -> true LRD.
Rice University | INCITE.rice.edu | April 2001
INCITE.rice.edu