AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an on-going demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based K-nearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AI-aided SD-IoT model provides superior QoS performance compared to previous approaches.