Algorithm Package of AI-Driven SDN Controller-Switch Load Balancing Strategies

Recently, Software-Defined Networking (SDN) is receiving much research attention due to its ability to decouple the data plane from the control architecture by associating the network switches to one (centralized) or more (distributed) controller(s). Traditionally, switches are assigned to the controllers in a static manner which results in under-utilization of the resources of the controllers and increased response delays to user requests. In this paper, we consider a practical load-balancing and agile scenario by formulating the dynamic associations of switches and controllers as an NP-hard optimization problem to minimize the maximum resource utilization of the controllers. Therefore, we propose an Ant Colony Optimization (ACO)-based algorithm to deal with the aforementioned request satisfiability issue in large SDN systems in polynomial-time. Furthermore, we envision a hybrid deep learning model consisting of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) structures to achieve near-optimal resource utilization for real-time SDN applications. Experimental results demonstrate that our customized CNN-GRU model outperforms the other techniques in terms of resource utilization (15% - 45% optimality gap) within a sianlficantly reduced computational running time (≤ 0.1s).