Traffic Engineering

Traffic engineering aims to optimize network performance by efficiently routing traffic across network links, minimizing congestion and maximizing throughput. Current research heavily utilizes machine learning, particularly graph neural networks and reinforcement learning (both single-agent and multi-agent), to address this challenge, often focusing on distributed optimization and robust solutions that adapt to unpredictable traffic demands and network failures. These advancements offer significant potential for improving the scalability, efficiency, and resilience of wide-area networks, impacting both the design of future networks and the operational efficiency of existing ones.