Dynamic load balancing in telemedicine using genetic algorithms and fog computing.

In the healthcare industry, telemedicine has emerged as a game-changing technology that makes medical treatments accessible from a distance. However, the critical factors for delivering high-quality healthcare are the assurance of low latency and efficient resource utilization. This research study presents a new methodology to tackle these obstacles by incorporating Genetic Algorithms (GAs) into a Fog Computing framework for dynamic load balancing in telemedicine systems. By conducting a series of experiments, we conducted a comparative analysis between this approach and conventional cloud and fog computing configurations. The findings illustrate that our proposed strategy exhibits persistent superiority over alternative methods, resulting in a notable latency reduction and improved resource use optimization. Our load balancing technique, based on genetic algorithms (GA), enhances the quality of telemedicine services by effectively adjusting to varying workloads and network circumstances. This adaptation capability can increase patient care and ensure scalability for upcoming healthcare systems. [ABSTRACT FROM AUTHOR]