Learning automata: an introduction
Learning automata: an introduction
Optical burst switching (OBS) - a new paradigm for an optical Internet
Journal of High Speed Networks - Special issue on optical networking
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Control architecture in optical burst-switched WDM networks
IEEE Journal on Selected Areas in Communications
Congestion window-based adaptive burst assembly for TCP traffic in OBS networks
Photonic Network Communications
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Optical Burst Switching (OBS) is widely believed to be the technology for the future core network in the Internet. Burst assembly time at the ingress node is known to affect the traffic characteristics and loss distribution in the core network. We propose an algorithm for adapting the burst assembly time based on the observed loss pattern in the network. The proposed Learning-based Burst Assembly (LBA) algorithm uses learning automata which probe the loss in the network periodically and change the assembly time at the ingress node to a favorable one. We use a discrete set of values for the burst assembly time that can be selected and assign a probability to each of them. The probability of selecting an assembly time is updated depending on the loss measured over the path using a Linear Reward-Penalty (LR-P) scheme. The convergence of these probabilities eventually leads to the selection of an optimal burst assembly time that minimizes the burst loss probability (BLP) for any given traffic pattern. We present simulation results for different types of traffic and two network topologies to demonstrate that LBA achieves lower BLP compared to the fixed and adaptive burst assembly mechanisms existing in the literature.