A new look at fault-tolerant network routing
Information and Computation
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Design of a Router for Fault-Tolerant Networks
PCRCW '94 Proceedings of the First International Workshop on Parallel Computer Routing and Communication
Correctness of a gossip based membership protocol
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
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This paper shows packet delivery rate can be improved by adopting learning-based hybrid routing strategies when a wired network suffers from severe link disruption. The dynamics of the link disruptions complicate the routing problem; successful and stable routing operations of conventional routing approaches are hindered as the level of disruption increases. The target is to develop a robust and efficient routing approach in a single structure. A robust routing approach means a packet should be delivered to a destination even under severe disruptions. Efficient routing should deliver a packet with the shortest path at no disruption. These goals should be achieved with the maximum utilization of preexisting network components and with the minimal human intervention once installed. Therefore, we chose a popular conventional routing scheme, Link State, and add-ons that can learn changing network environment. Our approach is to add a learning agent and a simple routing scheme to Link State in order to automatically select a better routing scheme at an arbitrary level of disruption. Markov Decision Process is employed to model this problem. The simulation results show robustness and packet delivery rate are increased up to 35% at acceptable cost of computational and architectural complexity even when Link State approach is close to be collapsed.