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- W3044536325 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." @default.
- W3044536325 created "2020-07-29" @default.
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- W3044536325 date "2020-08-10" @default.
- W3044536325 modified "2023-09-26" @default.
- W3044536325 title "Neural Packet Routing" @default.
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- W3044536325 doi "https://doi.org/10.1145/3405671.3405813" @default.
- W3044536325 hasPublicationYear "2020" @default.
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