Controller Selection Module for Switch Migration in Fog Computing Environments

Dynamic load balancing is critical in fog computing for increasing network demands and optimizing IoT (IoT) efficiencies. This study investigates the application of the Threshold-Aware Switch Migration (TASM) method, which integrates threshold parameters into the load balancing algorithm, aiming to enhance network performance. Through a comprehensive experimental setup, the research evaluates the TASM method's effectiveness compared to traditional approaches, focusing on critical metrics such as the Load Balancing Rate (LBR) and introducing the Load Threshold Compliance Rate (LTCR). The results reveal that incorporating the threshold settings leads to a nuanced outcome. While the initial adjustment shows a slight decline in LBR, the LTCR significantly improves, indicating a more optimized load distribution across the network. These findings underscore the importance of threshold settings in fog computing, demonstrating their potential to improve network stability and efficiency. This paper contributes to the fog computing literature by showcasing how strategic adjustments in load balancing parameters can directly enhance operational dynamics and suggesting avenues for future research in dynamic and adaptive network management strategies.