Cloud computing is currently a popular technology and has great research potential in various fields including resource allocation, task scheduling, security, and privacy. Among these research fields, task scheduling is a challenging issue because resources are limited. Different methods for scheduling tasks are provided. In this research, a cloud task scheduling algorithm is presented using a combination of gray wolf meta-heuristic optimization (GWO) and Tabu Search (TS) algorithms. The use of the TS algorithm has led to the improvement of the optimal responses of the GWO algorithm, and the result of this improvement is the reduction of the duration of tasks and the reduction of the overall delay of the system. Simulation results on 4 different scenarios show that our proposed method is 1.37 times faster than task scheduling methods based on meta-heuristic algorithms.
