Load Balancing of Servers in Software-defined Internet of Multimedia Things using the Long Short-Term Memory Prediction Algorithm

The increase of traffic in Internet-of multimedia Things networks leads to additional load on servers; therefore, this paper focuses on server load balancing in multimedia Internet-of-Things networks. Software-defined networking technology has been used to achieve load balancing in these networks, as software-defined networks with new features have improved load balancing in multimedia Internet-of-Things networks. In this study, the short-term and long-term recurrent neural network algorithm is used to predict the server load, and then a fuzzy system is used to accurately determine the server levels. Also, this article saves energy and also reduces server overhead.