Machine Learning for Networking: Workflow, Advances and Opportunities

Abstract

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.

8 pages, 2 figures

References in corpus (1)

• PCC: Re-architecting Congestion Control for Consistent High Performance

Cited by in corpus (21)

• RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
• Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
• Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
• Machine Learning for Intelligent Optical Networks: A Comprehensive Survey
• A Survey on Recent Advances in Transport Layer Protocols
• Deep Learning in Mobile and Wireless Networking: A Survey
• RouteNet-Fermi: Network Modeling with Graph Neural Networks
• Machine Learning-based Link Fault Identification and Localization in Complex Networks
• MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
• Softwarization, Virtualization, & Machine Learning For Intelligent & Effective V2X Communications
• Joint Traffic-Aware UAV Placement and Predictive Routing for Aerial Networks
• Review and Analysis of Recent Advances in Intelligent Network Softwarization for the Internet of Things
• The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
• Towards 6G Networks: Use Cases and Technologies
• Integrated Methodology to Cognitive Network Slice Management in Virtualized 5G Networks
• Traffic and Obstacle-aware UAV Positioning in Urban Environments Using Reinforcement Learning
• Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
• Scaling Graph-based Deep Learning models to larger networks
• A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms
• Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward
• Toward Intelligent Network Optimization in Wireless Networking: An Auto-learning Framework