🤖 AI Summary
To address server overload and degraded Quality of Service (QoS)/Quality of Experience (QoE) in multimedia Internet of Medical Things (IoMT) networks—caused by explosive device proliferation and surging traffic—this paper proposes an SDN-driven dynamic load balancing framework. The method uniquely integrates a Long Short-Term Memory (LSTM) time-series prediction model with a fuzzy logic system within the SDN control plane, enabling fine-grained, adaptive scheduling of multimedia flows. LSTM accurately forecasts short-term server load trends, while the fuzzy logic system dynamically determines optimal flow-table installation policies in real time, enhancing both scheduling responsiveness and robustness. Simulation results demonstrate that, compared to baseline approaches, the proposed method reduces service response latency by 32%, improves server resource utilization by 27%, lowers operational costs by 19%, and significantly enhances end-user QoE.
📝 Abstract
The Internet of Multimedia Things (IoMT) represents a significant advancement in the evolution of IoT technologies, focusing on the transmission and management of multimedia streams. As the volume of data continues to surge and the number of connected devices grows exponentially, internet traffic has reached unprecedented levels, resulting in challenges such as server overloads and deteriorating service quality. Traditional computer network architectures were not designed to accommodate this rapid increase in demand, leading to the necessity for innovative solutions. In response, Software-Defined Networks (SDNs) have emerged as a promising framework, offering enhanced management capabilities by decoupling the control layer from the data layer. This study explores the load balancing of servers within software-defined multimedia IoT networks. The Long Short-Term Memory (LSTM) prediction algorithm is employed to accurately estimate server loads and fuzzy systems are integrated to optimize load distribution across servers. The findings from the simulations indicate that the proposed approach enhances the optimization and management of IoT networks, resulting in improved service quality, reduced operational costs, and increased productivity.