Introduction to AI Traffic Engineering Beginner

Traffic engineering is the practice of optimizing how traffic flows through a network. Traditional TE relies on static policies, manual MPLS tunnels, and reactive adjustments. AI transforms this into a proactive, adaptive system that predicts demand, optimizes routes in real time, and maintains optimal performance across changing conditions.

Traditional vs. AI-Driven Traffic Engineering

AI Techniques for Traffic Engineering

1. Supervised learning

   Traffic classification, application identification, and demand prediction from historical data.

2. Reinforcement learning

   Dynamic routing optimization where an agent learns optimal traffic distribution policies through network interaction.

3. Deep learning

   Complex pattern recognition in traffic flows using CNNs (spatial patterns) and LSTMs (temporal patterns).

4. Graph neural networks

   Model network topology directly and learn optimal routing on graph structures.