multiscale traffic processing techniques for network inference and control
DESCRIPTION
Multiscale Traffic Processing Techniques for Network Inference and Control. Richard Baraniuk, Edward Knightly, Robert Nowak, Rolf Riedi Rice University July 2000. Rice Networking Research. INCITE (RB, EK, RN, RR, Coates) Scalable QoS (EK) Multi-tier (Aazhang, Wallach, RB, EK, RR) - PowerPoint PPT PresentationTRANSCRIPT
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Multiscale Traffic Processing Techniques for Network Inference and Control
Richard Baraniuk, Edward Knightly, Robert Nowak, Rolf Riedi
Rice UniversityJuly 2000
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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)
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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
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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
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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
_________ _________
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Rice University INCITE Project, July 2000
Multiscale Modeling
Time
Scale
\/||
Innovative synthesis: multiplicative
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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
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Matching of Marginals Real Trace Multiplicative Models: Additive Models: match marginals closely match only variance
6ms 6ms
12ms 12ms
24ms 24ms
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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”
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Rice University INCITE Project, July 2000
Multiscale Queuing
MSQ formula: for all scales (non-asymptotic)
predictive capability
revolutionary queuing approach
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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
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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
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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
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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