Abstract For optical networks, routing and resource allocation which considerably determines the resource efficiency and network capacity is one of the most important works. It has been widely studied and many excellent algorithms have been developed. However, theoretical analysis shows that routing and resource allocation belongs to the Nondeterministic Polynomial Complete (NP-C) problem no matter in wavelength division multiplexing (WDM) optical networks, elastic optical networks (EONs), or space division multiplexing (SDM) optical networks. At presents, there doesn't exist a polynomial-time algorithm for routing and resource allocation. In recent years, machine learning which shows great advantages in solving complex problems has been widely concerned and researched. Using machine learning to conduct routing and resource allocation has aroused a great interest of researchers. This paper provides an overview on routing and resource allocation based on machine learning in optical networks. At first, we briefly introduce the routing and wavelength allocation (RWA) problem in WDM optical networks, the routing and spectrum allocation (RSA) problem in EONs, and the routing, core, spectrum allocation (RCSA) problem in SDM optical networks respectively. Commonly used machine learning techniques in optical networks are briefly elaborated. Then, the problems of quality of transmission (QoT) estimation, traffic estimation, and crosstalk prediction which can help to routing and resource allocation are also elaborated. The machine learning enabled RWA algorithms, RSA algorithms, and RCSA algorithms are elaborated, analyzed and compared in detail. In addition, the applications of machine learning in the QoT estimation, traffic estimation, and crosstalk prediction, etc., are also elaborated. Based on the existing research results, we present future research directions about how to use machine learning techniques to conduct routing and resource allocation in multidimensional time–space-frequency optical networks and satellite optical networks.

中文翻译：

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光网络中基于路由和资源分配的机器学习概述

摘要 对于光网络而言，路由和资源分配是决定资源效率和网络容量的重要工作之一。它得到了广泛的研究，并开发了许多优秀的算法。然而，理论分析表明，无论是波分复用（WDM）光网络、弹性光网络（EON）还是空分复用（SDM）光网络，路由和资源分配都属于非确定性多项式完全（NP-C）问题。 . 目前，还没有一种用于路由和资源分配的多项式时间算法。近年来，机器学习在解决复杂问题方面显示出巨大优势，受到了广泛的关注和研究。利用机器学习进行路由和资源分配引起了研究人员的极大兴趣。本文概述了光网络中基于机器学习的路由和资源分配。首先，我们分别简要介绍了WDM光网络中的路由和波长分配（RWA）问题、EON中的路由和频谱分配（RSA）问题以及SDM光网络中的路由、核心、频谱分配（RCSA）问题。简要阐述了光网络中常用的机器学习技术。然后，还详细阐述了有助于路由和资源分配的传输质量（QoT）估计、流量估计和串扰预测问题。详细阐述了启用机器学习的 RWA 算法、RSA 算法和 RCSA 算法，进行了详细的分析和比较。此外，还详细阐述了机器学习在QoT估计、流量估计、串扰预测等方面的应用。在现有研究成果的基础上，我们提出了如何利用机器学习技术在多维时空频光网络和卫星光网络中进行路由和资源分配的未来研究方向。