不确定性环境中医疗物联网资源云调度的仿真研究

Simulation Research of Cloud Resource Scheduling with Medical Internet of Things under Uncertainty

为了在不确定性情况下提高云医疗物联网资源调度及负载的均衡,在研究系统不确定模型、遗传算法和贪心算法基础上,设计一种基于改进遗传算法的云物联网资源调度方案。通过将本文所提算法与GA、Min-Min和FCFS比较,所提算法减少了总体执行时间,以最小的定价成本实现不确定性下医疗物联网云资源上的负载均衡。

In order to improve the medical Internet of Things resource scheduling and load balancing problem by cloud technology under uncertainty,a cloud resource scheduling scheme with Internet of Thingsis is designed based on improved genetic algorithm and system uncertainty model,genetic algorithm and greedy algorithm.In the comparing simulation experiment for the proposed algorithm with GA,Min-Min and FCFS,the results show that the proposed algorithm reduces the overall execution time and achieves the load balancing of medical IoT cloud resources under uncertainty with the minimum pricing cost.