Healthcare Task Allocation in Cloud-based System Based on an Improved Grey Wolf Optimization by Angular Acceleration Concept

The Internet of Things (IoT) is a crucial technology widely utilized in various sectors in recent years.One significant application is in the healthcare system, particularly in mobile health and remote patient monitoring for individuals with different medical conditions such as kidney disease, heart disease, cancer, hypertension, diabetes, respiratory issues, and stroke.Integrating IoT with cloud computing can enhance the efficiency of healthcare systems and facilitate the creation of novel applications in the future.Load balancing is a significant issue in cloud computing systems that must be addressed.Solving the problem will decrease response time, power usage, and cost and improve server availability.This work consists of developing and executing a healthcare system utilizing IoT and addressing the load-balancing issue in cloud computing using an Improved version of the Grey Wolf Optimization (GWO) algorithm.The suggested method is named Improved GWO Virtual Machine Selector (IGWO-VMS).The proposed method chooses the Optimal Virtual Machine (VM) from a set of VMs based on its fitness value.Various tasks are prioritized and allocated to the most suitable VMs according to their Instruction Millions (IM), with tasks with high IM being assigned to VMs with high fitness values.The results showed that the suggested technique decreases latency and packet loss while maximizing throughput in healthcare systems.The efficiency and success of this technology surpass other state-of-the-art methods in decreasing makespan time and total processing time and ensuring load balancing across virtual machines.The GoCJ dataset used contain a number of jobs in terms of Million Instructions (MI) obtained from the workload behaviour witnessed in google cluster trace.The values of makespan time, throughput time and Standard Deviation (STD) where (23.05), (899.8979) and (177.7675),respectively, in case of applying 500, 600 and 900 tasks.