Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Statistical Multiplexing of Self-Similar Video Streams: Simulation Study and Performance Results
MASCOTS '98 Proceedings of the 6th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
A user-friendly self-similarity analysis tool
ACM SIGCOMM Computer Communication Review
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Feature selection for optimizing traffic classification
Computer Communications
Multimedia Tools and Applications
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This paper describes a step-by-step improvement process of a predictive connectionist module applied in the context of Video on Demand Service Providers (VDSP) applications. The aim at the end is to provide a methodology based on estimation to right size bandwidth usage. The improvement methodology [G. Gomes, Un Modele Connexioniste pour la Prediction e l'Optimization de la Bande Passante: Approche Basee sur la Nature Autosimulaire du Trafic Video IP, doctorat thesis presented at Universite d'Evry - Val d'Essonne, France (2004)] consists of three phases named examples-based level, modular solution and HVS (Heuristic for Variable Selection) [Y. Bennani, M. Yacoub, Features selection and architecture optimization in connectionist systems, International Journal of Neural Systems 10(5) (2000) 379-395]. In the first phase the ''sliding and overlaying'' technique is used for enabling the Predictive Connectionist Module (PCM) to be aware of dynamic input. In the second phase a new connectionist network is added to the first one so that the proactive function with prediction could be achieved through a modular architecture. Finally, the HVS method is used in the third phase for identifying optimized connectionist models with higher accuracy. Simulations results are provided which cover learning, prediction and evaluation phases.