A task unloading strategy of IoT devices using deep reinforcement learning based on mobile cloud computing environment

Abstract

Aiming at the task unloading mode in cloud computing environment, the task unloading problem for IoT devices is studied. Through theoretical analysis, we can know that in the task unloading problem, it is usually contradictory to improve the utilization of cloud resources and reduce the task delay. In order to solve this problem, a task unloading scheme for Internet of things devices using deep reinforcement learning algorithm is proposed. The deep reinforcement learning algorithm is used to model the task unloading problem. The return value with weight is introduced into the algorithm, and the utilization rate of cloud resources and the delay of unloading task are weighed by adjusting the return value of the weight. First of all, the improved k-means clustering algorithm with weighted density is used to cluster the physical machines. The physical machines of each cluster have similar bandwidth and task waiting time. Then, deep reinforcement learning is used to select the best physical machine cluster from the current unloading tasks. Finally, the improved PSO algorithm is used to select the optimal physical machine from the optimal cluster, and Pareto is used to improve the convergence speed. Experimental results show that compared with the traditional method, the proposed algorithm has a good performance, and can achieve the goal of increasing the utilization of physical machine resources and reducing task delay.