Download Connections Education for a Networked World

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)
• 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
INCITE
InterNet 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
• Multifractal (Riedi et al. ’97)
–small time scale
–Network, protocol layer
–Control at Connection level
packet
scheduling
round-trip
time
• LRD (Willinger et al. ‘93)
– Large times
– Client behavior
– Bandwidth over Buffer
session
lifetime
network
management
10s msec
minutes
hours
_________
_________
< 1 msec
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
additive
multiplicative
1
Matching variances on all scales
10
Positive, bursty
100
Rice University INCITE Project, July 2000
Gaussian, LRD
Matching of Marginals
Real Trace
Multiplicative Models:
match marginals closely
Additive Models:
match only variance
6ms
6ms
12ms
12ms
24ms
24ms
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 on
dyadic 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
Rice University INCITE Project, July 2000
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