Bomin Mao, Zubair Md. Fadlullah, Fengxiao Tang, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani
IEEE Transactions on Computers, to appear, 2017
Publication year: 2017-06

Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a
cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs’
parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In
this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from
rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep
learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively
utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to
construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central
Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output
traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer
simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay,
throughput, and signaling overhead.