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

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

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

Rice University | INCITE Project | September 2000 Multifractal Wavelet Model (MWM) Random multiplicative innovations A j,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 Multiscale Traffic Trace Matching 4ms 16ms 64ms Auckland 2000MWM match scale

Rice University | INCITE Project | September 2000 Marginal Matching 4ms 16ms 64ms scale Auckland 2000MWMGaussian

Rice University | INCITE Project | September 2000 Multiscale Queuing

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

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

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

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

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

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

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

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