A Network-Aware Load Balancer Using Feedback Learning

In a wireless network, dynamic traffic allows explosive data to be transmitted from one system to another. System parameters, network-level configuration, routing parameters, network characteristics, and system load factors are all affected by volatile data. In today's big data era, traffic adaptation is an important research area in wireless communications. The availability of load-balancing sensors helps reduce delays, lower energy consumption, and shorten execution time. This work provides a load-balancing technique that leverages the sensors' computational capacity and the sources' requirements to maximize the utility of the sensors. To achieve high resource utilization, we use a convergence-based technique. The model may be able to produce improved performance compared to traditional approaches. In this article, a proactive action method using wireless configuration is proposed. The intelligent resource utilization by multiple sensor devices can help to cope with the exponential increase of wireless data communication in mobile devices. Using the learning approach, a solution for rate evaluation and wireless load-balancing design is presented.