A Conceptual Framework for Cloud Load Balancing of data in Remote Health Monitoring Systems

Abstract

Cloud load balancing is essential for improving the efficiency and dependability of remote health monitoring systems. Remote health monitoring systems have become essential in today's interconnected society since they allow for the monitoring and enhancement of healthcare results.   These systems consistently gather and send an extensive quantity of data from diverse sensors and devices, such as wearables and IoT devices, to healthcare practitioners or centralized databases for analysis and decision-making.   Cloud load balancing is the process of evenly distributing incoming data traffic among different resources or servers in a cloud environment.   Monitoring devices manage the large volumes of data processed and acquired by sophisticated medical sensors, while simultaneously ensuring performance factors such as throughput and latency.    Load balancing is utilized to effectively manage large amounts of data through the use of centralized and distributed methods.   Through the integration of cloud load balancing into remote health monitoring systems, healthcare organizations can guarantee the efficient distribution of data processing and analysis across numerous servers. This prevents any single server from becoming overwhelmed, thereby ensuring the system's high availability and responsiveness. Furthermore, cloud load balancing helps optimize the utilization of computing resources, ensuring that each server handles an appropriate amount of workload. This not only improves the performance and response time of the system but also enhances scalability, allowing the system to handle increasing data loads with more patients and devices. Hence, it is necessary to allocate the burden of the intelligent operational devices to avoid any possible lack of response. This study presents a cloud-based framework that aims to equally divide the burden among fog nodes. The system is designed to cater to the communication and processing requirements of intelligent real-time applications.