Smart Bandwidth Management using a Recurrent Neuro-Evolutionary Technique

The requirement for correct bandwidth allocation and management in a multitude of different communication mediums has generated some exceedingly tedious challenges that need to be addressed both intelligently and with innovative solutions. Current advances in high speed broadband technologies have manifold increased the amount of bandwidth required during successful multimedia streaming. The progressive growth of Neuro-Evolutionary techniques have presented themselves as worthy options to address many of the challenges faced during multimedia streaming. In this paper a Neuro-Evolutionary technique called the Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network(RCGPANN) is presented for prediction of future frame sizes. The proposed technique takes into account the traffic size trend of the historically transmitted data for future frame size prediction. The predicted frame size forms the basis for estimation of the amount of bandwidth necessary for transmission of future frame. Different linear regression and probabilistic approaches are employed to estimate the allocated bandwidth, while utilizing the predicted frame size. Our proposed intelligent traffic size prediction along with bandwidth estimation and management results in a 98% increased efficiency.