Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]
Danish Rafique,Luis Velasco +1 more
TLDR
This tutorial paper reviews several machine learning concepts tailored to the optical networking industry and discusses algorithm choices, data and model management strategies, and integration into existing network control and management tools.Abstract:
Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis.read more
Citations
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Journal ArticleDOI
6G and Beyond: The Future of Wireless Communications Systems
TL;DR: Significant technological breakthroughs to achieve connectivity goals within 6G include: a network operating at the THz band with much wider spectrum resources, intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, and pervasive artificial intelligence.
Journal ArticleDOI
An Optical Communication's Perspective on Machine Learning and Its Applications
TL;DR: The mathematical foundations of basic ML techniques from communication theory and signal processing perspectives are described, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use.
Journal ArticleDOI
A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning
TL;DR: This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications.
Journal ArticleDOI
A Tutorial on Machine Learning for Failure Management in Optical Networks
Francesco Musumeci,Cristina Rottondi,Giorgio Corani,Shahin Shahkarami,Filippo Cugini,Massimo Tornatore +5 more
TL;DR: This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management, and introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks.
Journal ArticleDOI
Machine learning for intelligent optical networks: A comprehensive survey
Rentao Gu,Zeyuan Yang,Yuefeng Ji +2 more
TL;DR: A detailed survey of existing applications of ML for intelligent optical networks is presented, classified in terms of their use cases, which are categorised into optical network control and resource management, and optical network monitoring and survivability.
References
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