Journal Article

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Shahriar, Nashid, Caicedo, Oscar M., Salahuddin, Mohammad A., Ayoubi, Sara, Limam, Noura, Estrada-Solano, Felipe, Boutaba, Raouf

2018

Overview

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

• Publisher: Springer London,Sociedade Brasileira de Computação,Brazilian Computing Society (SBC)
• Subject: Artificial intelligence / Computer Applications / Computer Communication Networks / Computer Science / Computer Systems Organization and Communication Networks / Congestion control / Cybersecurity / Information Systems and Communication Service / IT in Business / Machine learning / Processor Architectures / Quality of service architectures / Resource management / Traffic classification / Traffic congestion / Traffic prediction / Traffic routing