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

By Raouf Boutaba, Mohammad Ali Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada Solano, Oscar Mauricio Caicedo Rendon. Journal of Internet Services and Applications, 2018

955 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b2700142a375d6c347050c6d3ef7cf4d41b82a25

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Citations in the corpus

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, neutral): “[20] Springer 2018 501 ML in networking √ ≈ × √”
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks (2020, methodology): “63% higher than other centralized machine learning methods [23] in terms of the energy demand prediction accuracy.”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “…particular, the domain of network management presents a wide variety of problems that can be broadly categorized into: [91] Supervised Learning: Supervised learning is typically applied to problems relating to trafﬁc prediction [94] and classiﬁcation [95], as well as slice resource prediction [96].”
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks (2019, methodology): “We compare our proposed methods with some other conventional machine learning methods including decision tree (DT), random forest (RF), support vector regressor (SVR), k-neighbors regressor (KNR), stochastic gradient descent regressor (SGDR), and multi-layer perceptron regressor (MLPR) [18].”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, methodology): “The simulation results show that the root mean squared error of a clustered FEDL model is lower than conventional ML algorithms, e.g., multi-layer perceptron regressor [202].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “vey on AI applications in various areas of networking [33], and another survey focuses on deep learning (DL) based applications in wireless networking [34].”
State-of-the-art in artificial neural network applications: A survey (2018, background): “Most researchers applied machine learning (ML) techniques in solving classification problem [27, 28].”