CLO: Controller load optimization using multi-step load prediction for software-defined internet of things

Software-Defined Internet of Things (SD-IoT) is a novel network architecture that integrates Software-Defined Networking (SDN) with Internet of Things (IoT) technologies. As the network scales up, increasing service requests impose a heavier processing burden on the control plane, resulting in load imbalance among controllers. Existing switch migration mechanisms have been proposed to optimize controller load. Unfortunately, most current approaches rely solely on real-time network information or the network state in the next time period, which fails to identify overloaded controllers effectively. Moreover, they overlook the load variation trends of switches in the process of switch selection, leading to suboptimal results. More critically, existing methods often struggle to balance load balancing effectiveness and migration cost when selecting target controllers. To address these issues, we propose controller load optimization using multi-step load prediction (CLO) scheme. This scheme adopts the decomposition-based linear model (DLinear) for multi-step load prediction, which helps avoid unnecessary migrations. We further incorporate the Weighted Least Squares (WLS) method to analyze the load trend of each switch, enabling intelligent identification of candidate switches for migration. In addition, we propose a target controller selection algorithm based on an improved Zebra Optimization Algorithm (ZOA), which significantly reduces load imbalance and migration cost. Our approach is based on two assumptions. Firstly, all controllers cannot be overloaded simultaneously. Secondly, each switch can only be connected to one master controller. Under these assumptions, we conduct experiments using Mininet as the emulation platform and Ryu as the controller. Experimental results show that, compared with existing approaches, CLO scheme reduces the average load imbalance rate by 21.3 % and the average response time by 14.1 %.