Neural Packet Routing

Abstract

Deep learning has shown great potential in automatically generating routing protocols for different optimization objectives. Although it may bring superior performance gains, there exists a fundamental obstacle to prevent existing network operators from deploying it into a real-world network, i.e., the uncertainty of statistical nature in deep learning can not provide the certainty of basic connectivity guarantee required in real-world routing. In this paper, we propose the first deep-learning-based distributed routing system (named NGR) that can achieve the connectivity guarantee while still attaining the routing optimality. NGR provides a novel packet routing framework based on the link reversal theory. Specially-designed neural network structures are further proposed to seamlessly incorporate into the framework. We apply NGR in the tasks of shortest-path routing and load balancing. The evaluation results validate that NGR can achieve 100% connectivity guarantee despite the uncertainty of deep learning and gain performance close to the optimal solution.