A robust method for fitting the ( sigma; oarr;, rho; oarr;) model to a traffic source

TitleA robust method for fitting the ( sigma; oarr;, rho; oarr;) model to a traffic source
Publication TypeConference Paper
Year of Publication2003
AuthorsAzimi, M., P. Nasiopoulos, and R. Ward
Conference NameInformation Technology: Research and Education, 2003. Proceedings. ITRE2003. International Conference on
Pagination228 - 232
Date Publishedaug.
Keywordscommunication network, divide and conquer methods, mathematical programming, optimization, QoS, quality of service, sequential quadratic programming, telecommunication networks, telecommunication traffic, traffic characterization, traffic regulation, VBR traffic source

A communication network, which offers a deterministic QoS guarantee to VBR traffic sources, must use a traffic regulation scheme to reserve network resources for each source. The key component of a traffic regulation scheme is the traffic characterization model used to characterize the traffic of each source. The ( sigma; oarr;, rho; oarr;)model is so far the most popular traffic model used in communication networks. In order to achieve high network utilization, parameters of the traffic model should be selected carefully, such that the model specifies the actual traffic as accurately as possible. We present a novel method for selecting the parameters of a ( sigma; oarr;, rho; oarr;)model for a VBR traffic source. Our method strives for accuracy, implementation simplicity and execution speed as the design goals. Our approach consists of two parts: 1) constructing the empirical envelope of the source from the traffic, and 2) finding the model parameters from the empirical envelope. We present novel solutions for these two problems. Our method for constructing the empirical envelope is faster and more accurate than the presently existing methods and can be employed in real-time applications. Our method for finding the ( sigma; oarr;, rho; oarr;)model parameters from the empirical envelope is based on the 'divide and conquer' and sequential programming optimization techniques, and finds a near optimum result. The performance and accuracy of our methods were experimentally compared to other available methods. The results showed that the overall performance, specifically the speed and the accuracy of our methods, are significantly better than the current methods found in the literature.


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