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Analytical Performance Evaluation

The success of computer and communication systems strongly depends on their performance, typically reflected in the perception of speed. Optimizing system performance, subject to a set of resource and cost constraints, is thus a critical design goal for system engineers. An elegant technique to help in this matter is performance evaluation which can be performed either by measurements, simulation, or using theoretical methods. In particular, analytical performance evaluation has the fundamental merit of rapidly leading to rigorous and unequivocal insight into the behavior of systems which can be accordingly tuned and optimized.

Our own research is concerned with extending the theory of the stochastic network calculus, which is a probabilistic extension of the deterministic network calculus conceived by R. Cruz in the early 1990's. Over the past two decades the calculus has established itself as a versatile alternative methodology to the classical queueing theory for the performance analysis of computer and communication networks. Its prospect is that it can deal with problems that are fundamentally hard for queueing theory, based on the fact that it works with bounds rather than striving for exact solutions. We are in particular concerned with various fundamental research problems related to modelling and analyzing networks with flow transformations, or improving the bounds accuracy using refined inequalities. On the long term, we believe that our research can significantly contribute to establishing the stochastic network calculus as an indispensable mathematical tool for the performance analysis of resource sharing based systems.

Selected Publications

On the Convergence to Fairness in Overloaded FIFO Systems
Citation key CHC-OCFOFS-11
Author Ciucu, Florin and Hohlfeld, Oliver and Chen, Lydia Y.
Title of Book Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM '11)
Pages 1988–1996
Year 2011
ISBN 978-1-4244-9919-9
ISSN 0743-166X
DOI http://dx.doi.org/10.1109/INFCOM.2011.5935004
Location Shanghai, China
Address New York, NY, USA
Month April
Publisher IEEE
Abstract Many of computing and communication systems are based on FIFO queues whose performance, e.g., in terms of throughput and fairness, is highly influenced by load fluctuations, especially in the case of short-term overload. This paper analytically proves that, for both Markovian and heavy-tailed/self-similar arrivals, overloaded FIFO queues are asymptotically fair in the sense that each flow or aggregate of flows receives a weighted fair share over large time scales. In addition, the paper provides the corresponding transient results and convergence rates, i.e., the amount of time it takes for a flow to probabilistically attain the fair share. Interestingly, for Markovian arrivals, the paper indicates smaller convergence rates at higher utilizations, which is exactly the opposite behavior characteristic to underloaded queueing systems.
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