计算机网络路由层深度学习研究综述

Fengling Jiang, K. Dashtipour, A. Hussain
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引用次数: 16

摘要

随着过去一年深度学习领域的最新成就,许多计算机和网络应用积极使用深度学习架构,包括卷积神经网络和长短期记忆,以通过它们来提高其方法的性能。计算机网络是一个复杂的动态系统。例如,路由是通信网络领域的主要组网任务,它被广泛用于优化从原始主机到目的主机的最优路由。然而,传统的路由协议大多是基于专家的经验。本文概述了计算机网络中路由层的深度学习方法。此外,本文还讨论了网络路由的强化学习方法。最后,我们概述了当前最先进的方法以及一些未来的研究方向。
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A Survey on Deep Learning for the Routing Layer of Computer Network
With recent achievements in deep learning over the past year, many computer and network applications actively used deep learning architectures including convolution neural network and long short-term memory to improve the performance of their approach through them. The computer network used a complex and dynamic system. For example, routing is the main networking tasks in the fields of the communication network and it is widely used to optimize the optimal routing from the original host to the destination host. However, most of the traditional routing protocol is based on the experience of experts. In this paper, we present an overview of deep learning methods for the routing layer in the computer network. Furthermore, this paper discusses reinforcement learning methods about network routing. Finally, we outline a summary of the current state-of-the-art approaches along with some future research directions.
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