Overview on routing and resource allocation based machine learning in optical networks

Zhang, Yongjun; Xin, Jingjie; Li, Xin; Huang, Shanguo

doi:10.1016/j.yofte.2020.102355

Cited by 41 publications

(27 citation statements)

References 88 publications

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“…It summarizes the various challenges and major findings of these input data and ML methods. Zhang et al [31] presents diverse applications of ML in routing and resource allocation in optical networks, without any specific focus on SDN-enabled networks.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Routing Optimization in SDN

et al. 2021

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In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

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Section: Related Workmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Routing Optimization in SDN

et al. 2021

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“…Conversely, in UL (Bishop (2006)) there is no ground-truth output, and the algorithms normally attempt to discover patterns in the data. Reinforcement Learning aims to raise the cumulative reward so that it is more suitable for sequential decision-making tasks (Zhang 2020b). Supervised learning has regression and classification; unsupervised learning includes cluster analysis and dimensionally reduction, also Reinforcement Learning (RL) includes classification and control, as illustrated in Fig.…”

Section: Machine Learningmentioning

confidence: 99%

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

2021

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Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

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“…The efforts pertaining to the use of AI and Machine learning in resource allocation is well-documented in [51]. In [52], the authors proposed a dynamic fuzzy-based algorithm for EONs that utilize multi-core fibers.…”

Section: A Spectrum-aware Ra Schemesmentioning

confidence: 99%

Joint Minimization of Spectrum and Power in Impairment-Aware Elastic Optical Networks

et al. 2021

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Flexible-grid elastic optical networks (EONs) are widely considered as the future of next generation optical transport networks. Depleting spectral resources and increasing power consumption are two critical bottlenecks of EONs. In this paper, we propose a novel impairment-aware resource allocation scheme that jointly minimizes bandwidth waste and power consumption of EONs. The proposed scheme is presented in the form of an analytical optimization for small-sized networks. A low-complexity heuristic is then proposed for large networks. Numerical results show that by appropriately assigning the route, modulation format, and carrier frequency to each connection request, the proposed scheme significantly increases spectral efficiency and reduces power consumption in networks that handle varying traffic volumes.

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Overview on routing and resource allocation based machine learning in optical networks

Cited by 41 publications

References 88 publications

A Survey on Machine Learning Techniques for Routing Optimization in SDN

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Joint Minimization of Spectrum and Power in Impairment-Aware Elastic Optical Networks

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