Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
arXiv:2205.08347 · doi:10.1109/TNSM.2022.3169988
Abstract
The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.
References in corpus (20)
- XGBoost: A Scalable Tree Boosting System
- Federated Learning: Strategies for Improving Communication Efficiency
- Neural Architecture Search: A Survey
- Deep Learning in Spiking Neural Networks
- Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
- Generalizing from a Few Examples: A Survey on Few-Shot Learning
- Towards Robust Interpretability with Self-Explaining Neural Networks
- Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
- When Machine Unlearning Jeopardizes Privacy
- Interpreting Deep Learning-Based Networking Systems
- Taurus: A Data Plane Architecture for Per-Packet ML
- Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
- Towards Probabilistic Verification of Machine Unlearning
- pForest: In-Network Inference with Random Forests
- Heterogeneous Data-Aware Federated Learning
- Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Update
- Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
- FENXI: Deep-learning Traffic Analytics at the Edge
- Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure
- Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps