An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources

Doulamis, AD; Doulamis, ND; Kollias, SD

dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T01:18:37Z
dc.date.available	2014-03-01T01:18:37Z
dc.date.issued	2003	en
dc.identifier.issn	1045-9227	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15114
dc.subject	Adaptive Neural Network	en
dc.subject	Autoregressive Model	en
dc.subject	Community Networks	en
dc.subject	Computational Complexity	en
dc.subject	Digital Video	en
dc.subject	Estimation Algorithm	en
dc.subject	Generic Model	en
dc.subject	High Efficiency	en
dc.subject	Mpeg Video	en
dc.subject	Multimedia Services	en
dc.subject	Network Design	en
dc.subject	Traffic Characterization	en
dc.subject	Traffic Model	en
dc.subject	Traffic Prediction	en
dc.subject	Internet Protocol	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Computational complexity	en
dc.subject.other	Internet	en
dc.subject.other	Motion pictures	en
dc.subject.other	Network protocols	en
dc.subject.other	Telecommunication traffic	en
dc.subject.other	Video sources	en
dc.subject.other	Neural networks	en
dc.title	An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TNN.2002.806645	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TNN.2002.806645	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates t he network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Neural Networks	en
dc.identifier.doi	10.1109/TNN.2002.806645	en
dc.identifier.isi	ISI:000180862400014	en
dc.identifier.volume	14	en
dc.identifier.issue	1	en
dc.identifier.spage	150	en
dc.identifier.epage	166	en