Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decisionmaking problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.

翻译：流量工程是平衡网络流量的有效技术，能提升混合软件定义网络的性能。现有TE解决方案主要采用启发式算法，在静态流量需求下集中优化链路权重设置或流量分割比例。值得注意的是，随着网络规模扩大与管理复杂度提升，集中式TE方法在网络流量需求动态波动或发生故障时，存在计算开销高、路由优化响应时间长的问题。为实现TE中自适应高效路由，我们提出多智能体强化学习方法CMRL，将大规模网络的路由优化分解为多个小规模路由决策问题。为协调多个智能体实现全局优化目标，我们构建了交互式训练环境，使拥有部分链路利用率观测值的路由智能体获得训练。针对多智能体信用分配优化，引入差异奖励分配机制激励智能体采取更优动作。基于真实流量轨迹的大量仿真表明，CMRL在提升TE性能方面具有显著优势，尤其在流量需求变化或网络故障场景下表现优异。