CORE: Prediction-Based Control Plane Load Reduction in Software-Defined IoT Networks
Ilora Maity, Sudip Misra, Chittaranjan Mandal · IEEE Transactions on Communications · 2020
In this paper, we propose a scheme to address the problem of load management in the control plane of Software-Defined Internet of Things (SDIoT) networks. In SDIoT, multiple controllers are deployed to enhance network scalability. With the growth of IoT, the number of devices is increasing rapidly. The management of control plane load is an essential issue for IoT networks because of the dynamic traffic characteristics. IoT traffic is highly dynamic due to the heterogeneity of IoT devices in terms of mobility, activation model, Quality of Service (QoS) demand, and flow generation rate. The challenge is to prevent controller overload and distribute traffic optimally under the consideration of heterogeneous IoT devices. The proposed scheme estimates control plane load based on the mobility and activation model of IoT devices. For mobility prediction, we use Order- m fallback Markov Predictor as it consumes less space and performs efficiently even for small values of m. Based on the prediction results, we implement a traffic-aware rule-caching mechanism and a master controller assignment scheme to reduce the control plane load. Simulation results show that the proposed scheme reduces the peak intensity of the control traffic by 23.08% and 16.67%, as compared to the considered benchmark schemes.