QCMP: load balancing via in-network reinforcement learning

Traffic load balancing is a long time networking challenge. The dynamism of traffic and the increasing number of different workloads that flow through the network exacerbate the problem. This work presents QCMP, a Reinforcement-Learning based load balancing solution. QCMP is implemented within the data plane, providing dynamic policy adjustment with quick response to changes in traffic. QCMP is implemented using P4 on a switch-ASIC and using BMv2 in a simulation environment. Our results show that QCMP requires negligible resources, runs at line rate, and adapts quickly to changes in traffic patterns.