EFLB-IIoT: Enhanced Flow Control and Load Balancing Approach for SDN-Enabled Industrial Internet-of-Things

Software-defined networks (SDN) provide an efficient network architecture by enhancing global network monitoring and performance through the separation of the control plane from the data plane. In extensive SDN implementations for the Internet-of-Things (IoT), achieving high scalability and reducing controller load necessitates deploying multiple distributed controllers that collaboratively manage the network. Each controller oversees a subset of switches and gathers information about these switches and their interconnections, which can lead to imbalances in link and controller loads. Addressing these imbalances is crucial for improving quality of service (QoS) in SDN-enabled Industrial Internet-of-Things (IIoT) environments. In this paper, we present the NP-hardness of the link and controller load balancing routing (LCLBR) problem within IIoT. To tackle this issue, we propose an enhanced flow control and load balancing approach for SDN-enabled Industrial Internet-of-Things (EFLB-IIoT). EFLB-IIoT is an approximation-based technique that effectively maintains network activity among distributed controllers. Simulation results indicate that our proposed strategy reduces the maximum link load by 76% and the maximum controller response time by 85% compared to existing techniques, demonstrating superior performance over state-of-the-art methods.