A MeasurementAnalytic Approach for QoS Estimation (Completed)
Do Young Eun
(Dept. of ECE, North Carolina State University)
Ness B. Shroff
(Dept. of ECE, Purdue University)
Publications
Research Summary
In this work, we have developed a measurementanalytic framework in
which we are able to accurately estimate the queuelength
distribution (QLD) at any node where a moderate to a large
number of flows are aggregated. In the literature,
the notion of the dominant time scale (DTS) (or critical or
relevanttime scale) has been useful in estimating the QLD.
The DTS corresponds to the timescale relevant for describing
the queueing behavior based on particular network configurations, and
it is closely related to the concept embodied in the statement
"rare event occurs only in the most probable way." In particular,
the QLD depends only on the input statistics up to the DTS.
This feature of the DTS led researchers to believe that
correlations of input traffic only up to the DTS really matter
in estimating the QLD, and it was taken as a way of compromising
the dichotomy between the realms of SRD (shortrange dependent)
and LRD (longrange dependent) models.
However, we noted that this in fact could be misleading.
Since the DTS is itself defined as a global maximizer of
certain statistics of the input traffic over all time,
we would still need to know (or estimate by measurements) the
statistics over all time to find the DTS, thereby defeating the
original feature of the DTS. This in essence results in a
chickenandanegg type of cycle, which appears to make the
problem hopeless. However, we developed a stopping criterion to
successfully break this cycle and obtained a bound on the DTS.
Thus, our result has significant implications for network
measurements in that we only need to measure the statistics of
the traffic up to the bound on the DTS, in order to accurately estimate
the QLD at any point in the network. We also extended this work
to general workconserving scheduling schemes such as priority
queueing and Generalized Processor Sharing (GPS).
Some Related Papers

J. Choe and N. B. Shroff, "Use of the supremum distribution of Gaussian processes in queueing analysis with longrange dependence and selfsimilarity,"
Stochastic Models, vol. 16, Issue No. 2, February 2000.
 J. Choe and N. B. Shroff,
"On the Supremum Distribution of Integrated Stationary Gaussian Processes
with Negative Linear Drift, "
Advances in Applied Probability, March 1999, vol. 31, pp. 134156.
 J. Choe and N. B. Shroff,
"A CentralLimitTheorem Based Approach for Analyzing Queue Behavior in
ATM Networks," IEEE/ACM Trans. on Networking, vol. 6, no. 5,
October 1998, pp. 659671.
 H. Kim and N. B. Shroff,
"Loss Probability Calculations at a Finite Buffer Multiplexer,"
IEEE/ACM Trans. on Networking, vol. 9, no. 6, Dec. 2001,
pp. 765768.
 M. Grossglauser and JC. Bolot, "On the Relevance of LongRange
Dependence in Network Traffic," IEEE/ACM Trans. on Networking,
vol 7, no 5, October 1999.
 C. Courcoubetis, V.A. Siris, and G. Stamoulis,
"Application of the many sources asymptotic and effective bandwidths
for traffic engineering," Telecommunications Systems, 12
(1999) 167191.
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