Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning - Zongxuan SHA; Ru HUO; Chuang SUN; Shuo WANG; Tao HUANG (Tongxin xuebao)
The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding efficiency of the SDN switch will be reduced, which will affect the end-to-end transmission delay.To solve the above problems, the forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning was proposed.First, the switch state was integrated into the perception model, and the mapping relationship between switch state information and forwarding efficiency was established based on neural network.Then, combined with network state and traffic information, traffic scheduling policy was generated through deep reinforcement learning.Finally, the expert samples generated by the shortest path and load balance algorithms could guide the model training, which enabled the model to learn knowledge from expert samples to improve performance and accelerated the training process.The experimental results show that the proposed algorithm not only reduces the average end-to-end transmission delay by 15.31%, but also ensures the overall load balance of the network, compared with other algorithms.