Reinforcement learning-based load shared sequential routing

Authors:
Fariba Heidari;Shie Mannor;Lorne G. Mason
Affiliations:
Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada
Venue:
NETWORKING'07 Proceedings of the 6th international IFIP-TC6 conference on Ad Hoc and sensor networks, wireless networks, next generation internet
Year:
2007

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Abstract

We consider event dependent routing algorithms for on-line explicit source routing in MPLS networks. The proposed methods are based on load shared sequential routing in which load sharing factors are updated using learning algorithms. The learning algorithms we employ are either based on learning automata or on online learning algorithms that were originally devised for solving the adversarial multi-armed bandit problem. While simple to implement, the performance of the proposed learning algorithms in terms of blocking probability compares favorably with the performance of other event dependent routing methods proposed for MPLS routing such as the Success-to-the-top algorithm. We demonstrate the convergence of one of the learning algorithms to the user equilibrium within a set of discrete event simulations.