Technical Report Quality of Service Routing in MPLS Networks using Decentralized Learning

This paper presents several decentralized learning algorithms for on-line intra-domain routing of bandwidth guaranteed paths in MPLS networks when there is no a-priori knowledge of traffic demand. The presented routing algorithms use only their locally observed events and update their routing policy using learning schemes. The employed learning algorithms are either learning automata or the multi-armed bandit algorithms. We investigate the asymptotic behavior of the proposed routing algorithms and prove the convergence of one of them to the user equilibrium. Discrete event simulation results show the merit of these algorithms 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 policies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We provide 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 provide efficient, stable and scalable approaches for routing the bandwidth