Multiscale Traffic Processing Techniques for Network Inference and Control

Richard Baraniuk, Edward Knightly, Robert Nowak, Rolf Riedi

Rice UniversityJuly 2000

Rice University INCITE Project, July 2000

Rice Networking Research

• INCITE (RB, EK, RN, RR, Coates) • Scalable QoS (EK)

• Multi-tier (Aazhang, Wallach, RB, EK, RR)

• ScalaServer (Druschel, Zwaenepoel)

• Mobile IP (Dave Johnson)

Rice University INCITE Project, July 2000

Technical Challenges

State of network is intractable on a per-flow basis

Poor understanding of the origins of complex network dynamics

Lack of adequate modeling frameworks for network dynamics

Manageable, reduced complexity model with known accuracy

Rice University INCITE Project, July 2000

INCITEInterNet Control and Inference Tools at the Edge

• Overarching Objective– edge-based network measurement – modeling, monitoring, inference and control – scalable, real-time, online algorithms– (www.ece.rice.edu/INCITE)

• Current DARPA Project Goals– novel traffic models: realistic, manageable – capture multiscale variability and burstiness – provide basis for a novel queuing approach

and a intelligent probing strategy – synthesis and inference

Rice University INCITE Project, July 2000

Multiscale Nature of Traffic

• LRD (Willinger et al. ‘93)– Large times – Client behavior– Bandwidth over Buffer

packetscheduling

sessionlifetime

networkmanagement

round-triptime

< 1 msec 10s msec minutes hours

• Multifractal (Riedi et al. ’97)–small time scale–Network, protocol layer –Control at Connection level

_________ _________

Rice University INCITE Project, July 2000

Multiscale Modeling

Time

Scale

\/||

Innovative synthesis: multiplicative

Rice University INCITE Project, July 2000

Modeling on all Time Scales

real trace MWM FGn

1

10

100

multiplicative additive

Matching variances on all scales

Positive, bursty Gaussian, LRD

Matching of Marginals Real Trace Multiplicative Models: Additive Models: match marginals closely match only variance

6ms 6ms

12ms 12ms

24ms 24ms

Rice University INCITE Project, July 2000

MultiScale Queuing approach

Queue-length = supr(Kr - rc)

Kr = aggregate arrival in r time unit

difficulties: non-linearity & correlated events

MSQ key insight (SigMetrics, InfoComm)

For MWM – traffic: overflows ondyadic times are “independent”

Rice University INCITE Project, July 2000

Multiscale Queuing

MSQ formula: for all scales (non-asymptotic)

predictive capability

revolutionary queuing approach

Rice University INCITE Project, July 2000

Cross-traffic: Probing at Edge

Abstraction of connection: multiscale statistical model of delay and loss

Chirps of Probes: meet key protocol timing maximize inference capability

MSQ: from queuing delay infer cross-traffic

-> improved control

Rice University INCITE Project, July 2000

Multifractals: A Hand on Bursts

• Multifractals– Classify burstiness

(quantitative and qualitative)

– Captures non-Gaussianity– Multifractal models:

parsimonious, tractable & realistic

– New understanding– Novel statistical tools

Rice University INCITE Project, July 2000

INCITE: Deliverables

• Multifractal Analysis Toolbox– Wavelet based estimators with known accuracy

• Traffic Synthesis Software– Rapid multifractal algorithms

• Network Path Modeling Toolbox– Online Inference of competing cross-traffic

Challenges Improvements of algorithm

Adaptive Passive monitoring Deal with loss

Effect of network conditions on accuracy of inference

Impact

• INCITE project has promise to transform easily deployable COTS networks into predictable, controllable, and well-understood systems

www.ece.rice.edu/INCITE /DARPA