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• W4386410936 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." @default.
• W4386410936 created "2023-09-05" @default.
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• W4386410936 date "2023-09-01" @default.
• W4386410936 modified "2023-09-30" @default.
• W4386410936 title "PBCLR: Prediction-based control-plane load reduction in a Software-Defined IoT Network" @default.
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• W4386410936 doi "https://doi.org/10.1016/j.iot.2023.100934" @default.
• W4386410936 hasPublicationYear "2023" @default.
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