PBCLR: Prediction-based control-plane load reduction in a software-defined IoT network

Abstract

The exponential growth of devices and applications in Internet of Things (IoT) networks has caused control-plane traffic to escalate. Experts have suggested Software-Defined Networking (SDN) as a solution for complicated IoT network management. Nevertheless, SDN encounters obstacles in managing the substantial control-plane traffic many IoT devices produce. Prior findings have identified the dynamic switch migration technique as a viable solution for control plane load oscillation. However, their tendency to migrate switches during instances of controller overload restricts the efficiency of traditional migration strategies. As a result, these approaches lead to inefficiencies in the switch migration process, resulting in elevated latency. The present study introduces a prediction-based novel approach for mitigating the control-plane workload by leveraging the dynamic switch migration technique. The proposed method proactively and predictively migrates the switches anticipated to generate excessive control-plane traffic to an alternative controller. We use the learning technique along with time-series analysis to predict the future workload based on the historical control-plane traffic data. The proposed methodology is evaluated through simulation and the results demonstrate that the proposed method outperforms conventional techniques in enhancing load balancing efficacy on a distributed control plane and decreasing migration cost and controller response time by 30.6% on average.