Prioritized Experience Replay-Based Deep Deterministic Policy Gradient for Reliable Path Selection in SDN-IoT Networks

Abstract

Routing optimization is becoming prominent in Software-Defined Networks (SDN) due to the exponential growth of network traffic demands and the requirement for Quality of Service (QoS). However, reliable routing that satisfies the QoS requirements, such as end-to-end delay, packet loss, and bandwidth, remains a difficult task in SDN. To overcome this limitation, a Deep Reinforcement Learning (DRL)-based Prioritized Experience Replay-based Deep Deterministic Policy Gradient (PER-DDPG) model is proposed to enhance the routing performance in SDN with Internet of Things (SDN-IoT) with QoS requirements. Initially, requests are received and prioritized using the postponement strategy technique in the SDN controller, and the weights of the links are evaluated using the DRL method. Then, a routing path is identified by the routing algorithm, and requests in the queue are released using the time-strategy technique. Hence, reliable routing in an SDN with QoS requirements is accomplished. The proposed routing model based on DRL is evaluated by utilizing the end-to-end latency, throughput, and packet loss.