Multifractal Cross-Traffic Estimation
Vinay Ribeiro (vinay@rice.edu)
Electrical and Computer Engineering, Rice University
Mark Coates (mcoates@rice.edu)
Electrical and Computer Engineering, Rice University
Rolf Riedi (riedi@rice.edu)
Electrical and Computer Engineering, Rice University
Shriram Sarvotham (shri@rice.edu)
Electrical and Computer Engineering, Rice University
Brent Hendricks (brentmh@rice.edu)
Electrical and Computer Engineering, Rice University
Richard Baraniuk (richb@rice.edu)
Electrical and Computer Engineering, Rice University
In this paper we develop a novel model-based technique, the Delphi algorithm,
for inferring the instantaneous volume of competing cross-traffic across an end-to-end path.
By using only end-to-end measurements, Delphi avoids the need for data collection
within the Internet.
Unique to the algorithm is an efficient exponentially spaced probing
packet train and a parsimonious multifractal parametric model for the
cross-traffic that captures its multiscale statistical properties (including
long-range dependence) and queuing behavior. The algorithm is adaptive; it requires
no a priori traffic statistics and effectively tracks changes in network conditions.
ns (network simulator) experiments
reveal that Delphi gives accurate cross-traffic
estimates for higher link utilization levels while at lower utilizations
it over-estimates the cross-traffic. Also, when Delphi's
single bottleneck assumption does not hold it over-estimates the cross-traffic.