AI and Machine Learning landscape for networks [infographic]

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• potential vulnerabilities detection/auditing (e.g. due to outdated software, etc.)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.