Revolutionizing load management in SDIoT networks: a multicriteria approach for SDN controller

Abstract

In response to the burgeoning complexity of Internet of Things (IoT) networks, this study addresses the pressing issue of load management within Software Defined Networking (SDN) controllers. IoT networks, marked by high mobility and the extensive size of nodes, often exhibit load imbalances among SDN controllers, undermining their performance and efficiency. This research introduces a novel load-balancing technique to counter these challenges. Central to this approach is a unique load estimation method, which averages the load across SDN controllers, thereby facilitating effective load distribution. The performance of the proposed technique is evaluated based on the degree of load balancing, network response time, and migration cost. These metrics provide insights into the method's efficacy in maintaining load stability and the overhead associated with the migration process. Through this research, we aim to enhance load distribution management in IoT networks, which is essential for their ongoing evolution and progress. The proposed approach showed that the load imbalance degree of the proposed method decreased by an average of 53.3% and 43.5% compared to EASM and SMS respectively.