Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning
Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning
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DOI：
10.1109/tc.2017.2709742
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发表时间：
2017-11-01
影响因子：
3.7
通讯作者：
Mizutani, Kimihiro
中科院分区：
文献类型：
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作者：
Mao, Bomin;Fadlullah, Zubair Md.;Mizutani, Kimihiro
近年来，软件定义路由器（SDR）（可编程路由器）已经成为一个可行的解决方案，以提供一个具有简单的可扩展性和可编程性的成本效益的分组处理平台。多核平台显著提升SDR的并行计算能力，使其能够采用人工智能技术，即，深度学习，来管理路由路径。在本文中，我们探索了深度学习在数据包处理中的新机会，以廉价的方式将计算需求从基于规则的路由计算转移到基于深度学习的路由估计，以实现高吞吐量数据包处理。尽管深度学习技术已经在各个计算领域得到了广泛的应用，但到目前为止，研究人员还无法有效地将基于深度学习的路由计算用于高速核心网络。我们设想了一个有监督的深度学习系统来构建路由表，并展示了所提出的方法如何与使用中央处理器（CPU）和图形处理器（GPU）的可编程路由器集成。我们展示了我们独特的特征输入和输出流量模式如何通过分析和广泛的计算机模拟来增强基于深度学习的SDR的路由计算。特别是，仿真结果表明，我们的建议优于基准方法的延迟，吞吐量和信令开销。
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.