MARL-based Traffic Management in Communication Networks: Randomized vs. Traffic-Based Agent Deployment

The rapid growth of consumer applications such as video streaming, cloud gaming, Augmented Reality/Virtual Reality (AR/VR), and Internet of Things (IoT) deployments has intensified the demand for intelligent and adaptive congestion control in communication networks. Existing centralized congestion control solutions often struggle with scalability and responsiveness under highly dynamic traffic conditions. In this paper, we present a decentralized traffic engineering framework based on Multi-Agent Reinforcement Learning (MARL), tailored to optimize routing and traffic balancing in real time. The framework introduces two types of agents: (i) Routing Agents, which dynamically adjust link weights to steer flows across alternative paths, and (ii) Balancing Agents, which regulate traffic splitting ratios over precomputed multi-path routes. Beyond agent design, we address a key yet underexplored challenge: agent placement strategy. We compare random deployment versus traffic-aware deployment (based on node degree) and evaluate their effectiveness in mitigating congestion. Using an OMNeT++ simulation environment with realistic consumer traffic patterns on the Abilene topology (a well-known Internet2 research network), we test the framework across diverse congestion scenarios. Results show that traffic-aware placement consistently improves throughput, reduces latency and packet loss compared to both random placement and traditional Equal-Cost Multi-Path (ECMP) routing baselines. These findings highlight the potential of MARL-driven, placement-aware agents to support future consumer network- ing services requiring low-latency, high-reliability, and scalable resource management.