Artificial intelligence (AI) and machine learning (ML) is a trending topic in all technological domains. It offers a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks.
In this article we present a set of network issues for which the use of AI/ML methods can bring benefits and generally seems to make sense. Please note that this is a non-exhaustive list and is open to additions. For greater clarity, five main categories have been used. Also, the “Design-Build-Operate” model has been proposed as an additional perspective for classifying the network issues considered.
The network domains in which you can use AI & Machine Learning
Machine learning and artificial intelligence techniques can be used to solve issues in many aspects related to networking. Let's examine the five specified categories:
Network Planning & Service Provisioning (greenfield)
- network capacity planning
- determining network topology
- estimating/forecasting long-term trends in performance
- identification and classification of network nodes (e.g. for IoT)
- applying automated provisioning policies (i.e. pre-configured traffic or security profiles for clients/devices)
Traffic Policy Management
- traffic classification
- supporting intent-based networking
- user’s traffic characteristics prediction
- adjusting bandwidth and QoS dynamically
- traffic forwarding configuration (e.g. network load balancing)
Security
- applying appropriate network policies to devices
- malicious traffic detection
- potential vulnerabilities detection/auditing (e.g. due to outdated software, etc.)
- security anomalies detection/prediction (e.g. DDoS mitigation)
Problem Prevention & Solving
- detecting and eliminating false positives, true negatives (in alerting)
- making alerting more reliable (better than a threshold-based approach)
- identifying root causes for issues (multi-layer failure analysis)
- predictive maintenance (to prevent network degradations or outages)
- automatic recommendations for fault management (e.g. supporting network self-healing)
- external phenomena impact identification (mass events, ground works)
Network Optimization
- network topology optimization
- radio resources optimization (e.g. power-to-interference ratio optimization based on game theory)
- network capacity upgrade (e.g. predictive capacity tuning)
- traffic forwarding optimization (routes, paths, weights)
- energy consumption optimization (e.g. equipment switch on/off strategies, active/sleep scheduling)
- content cache optimization (e.g. content popularity assessment and proactive content loading)
Conclusion
We hope the above infographic will give you a better overview of where you can use ML and AI methods in the networks domain. Well-designed and implemented AI/ML techniques can benefit your network-based business. If you have questions about the practical aspects of AI/ML implementation for networks, feel free to contact us.