Evaluation of Hidden Markov Model Based Adaptive Provisioning of Optical Burst Switching Networks Amenable for Upgradation to Green Flexigrid Networks

Abstract

Catering to the evolving bandwidth-on-demand applications requires flexible provisioning architectures that ensure fairness to both high end and low end users of internet. Dynamic classification of network paths into long and short, based on traffic dependent connection holding times and a wavelength allocation from different subsets for these categories is a necessity for implementing energy saving hybrid switching, loss recovery and flexi grid bit rate variable schemes. Therefore, this study evaluates such a scheme of dynamic classification based on HMM predicted connection-holding times with tightly integrated adaptive burst sizing and segregated wavelength allocation for long and short categories. Simulation study of a 28 node OBS network shows that this coupled scheme reduces delays and results in throughput improvement of 67% for long and 12% for short traffic over schemes that employ adaptive burst sizing based on number of active traffic flows and independent wavelength assignment schemes.