A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks

Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research.