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A Case-Based Decision System for Routing in Packet-Switched Networks

Route planning with global optimization objectives in graphs is a challenging task with enormous computational complexity and finding the best solution is NP-complete. In addition, the network's operational performance varies whenever the environment changes. Traditional routing schemes fail to deal with these situations. We propose a case-based decision system for routing in packet switched networks to track the networking status. We also design a graph-aware neural network to suggest and revise the solutions from the past cases. The low-level structure of the neural network is learned by fitting with the features not only from each standalone vertex but also from the neighbors of each vertex. Experiments show that the proposed system outperforms state-of-art traffic-split and traffic-engineered routing schemes.