Toronto
Networking Seminar 2006
On the
Scaling of Statistical Delay Bounds
in Communication Networks
Florin
Ciucu
University of Virginia
Date:
February 17, 3pm
Location: BA1210 (Bahen Center)
Abstract
The stochastic network calculus is an evolving new methodology for
backlog and delay analysis of networks that can account for statistical
multiplexing gain. This talk presents recent advances on the
development of statistical network service curves, that can
express the service experienced by a traffic flow in a network
in terms of a probabilistic bound. The presented network service
curve permits the calculation of statistical end-to-end delay and
backlog bounds for broad classes of arrival and service distributions.
The benefits of the derived service curve are illustrated for the
exponentially bounded burstiness (EBB) traffic model. It is shown that
end-to-end performance measures computed with a network service curve
are bounded by O(H log H ), where H is the number of nodes traversed by
a flow. Using previously available techniques, which compute
end-to-end bounds by adding single node results, the corresponding
performance measures are bounded by O(H^3).
Bio:
Florin
Ciucu received the B.Sc. degree in Informatics from the Faculty of
Mathematics, University of Bucharest, in 1998, and the M.Sc. degree in
Computer Science from George Mason University, in 2001.
He is currently pursuing a Ph.D. degree in Computer Science at the
University of Virginia. He received the Best
Student Paper award at the ACM
Sigmetrics 2005 Conference.
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