Thispaperpresentsseveraldecentralizedlearningalgorithms for on-line intra-domain routing of bandwidth guaranteed paths in MPLS networks when there is no a-priori knowledge of traffic demand. The pre- sented routing algorithms use only their locally observed events and up- date their routing policy using learning schemes. The employed learning algorithms are either learning automata or the multi-armed bandit algo- rithms. We investigate the asymptotic behavior of the proposed routing algorithms and prove the convergence of one of them to the user equi- librium. Discrete event simulation results show the merit of these algo- rithms in terms of increasing the network admissibility compared with shortest path routing. We investigate the performance degradation due to decentralized routing as opposed to centralized optimal routing poli- cies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We pro- vide nonlinear programming formulations of these problems along with a distributed recursive approach to compute the solutions. An on-line partially-decentralized control architecture is also proposed to achieve the system optimal and the Nash bargaining solution performances. The results of this study indicate that decentralized learning techniques pro- vide efficient, stable and scalable approaches for routing the bandwidth guaranteed paths.
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