Reinforcing Reachable Routes

This paper studies the evaluation of routing algorithms from the perspective of reachability routing, where the goal is to determine all paths between a sender and a receiver. Reachability routing is becoming relevant with the changing dynamics of the Internet and the emergence of low-bandwidth wireless/ad-hoc networks. We make the case for reinforcement learning as the framework of choice to realize reachability routing, within the confines of the current Internet infrastructure. The setting of the reinforcement learning problem offers several advantages,including loop resolution, multi-path forwarding capability, cost-sensitive routing, and minimizing state overhead, while maintaining the incremental spirit of current backbone routing algorithms. We identify research issues in reinforcement learning applied to the reachability routing problem to achieve a fluid and robust backbone routing framework. This paper also presents the design, implementation and evaluation of a new reachability routing algorithm that uses a model-based approach to achieve cost-sensitive multi-path forwarding; performance assessment of the algorithm in various troublesome topologies shows consistently superior performance over classical reinforcement learning algorithms. The paper is targeted toward practitioners seeking to implement a reachability routing algorithm

Reinforcing Reachable Routes

Reachability routing is a newly emerging paradigm in networking, where the goal is to determine all paths between a sender and a receiver. It is becoming relevant with the changing dynamics of the Internet and the emergence of low-bandwidth wireless/ad hoc networks. This thesis presents the case for reinforcement learning (RL) as the framework of choice to realize reachability routing, within the confines of the current Internet backbone infrastructure. The setting of the reinforcement learning problem offers several advantages, including loop resolution, multi-path forwarding capability, cost-sensitive routing, and minimizing state overhead, while maintaining the incremental spirit of the current backbone routing algorithms. We present the design and implementation of a new reachability algorithm that uses a model-based approach to achieve cost-sensitive multipath forwarding. Performance assessment of the algorithm in various troublesome topologies shows consistently superior performance over classical reinforcement learning algorithms. Evaluations of the algorithm based on different criteria on many types of randomly generated networks as well as realistic topologies are presented