Effective Scheduling Simulation of Internet of Things Load Balanced Sharing of Resources

The balanced sharing of Internet of Things (IOT) resource loads can effectively improve the resource utilization rate of the IOT. Balanced sharing of resource loads must improve pheromone updating with an Internet virtual machine quality function in a load imbalance situation and complete Internet resource load scheduling. Traditional methods improve the heuristic information through IOT resource tasks and a resource matching degree function but do not focus on the improvement of resource pheromone updating, which leads to non-ideal scheduling effects. A type of IOT resource scheduling optimization method is proposed in this paper, which is based on the ant colony optimization algorithm. We consider various costs and factors in an IOT virtual machine; adjust states of the IOT virtual machine; calculate and adjust each resource's scheduling and dynamically schedule the IOT based on resource load history data and the current load condition of the IOT; realize the dynamic scheduling of IOT resources; improve the unbalanced load of an IOT virtual machine; improve pheromone updating with an Internet virtual machine mass function; and finally realize the load balanced sharing scheduling of IOT resources. Our experimental results show that the proposed method has obvious advantages in terms of task execution time and it can effectively maintain the load balanced sharing of resources on an IOT virtual machine.