عنوان مقاله [English]
Recently, fog computing has become a suitable platform for processing IoT applications. Thus, this architecture extends cloud computing services to the edge of the network, and processes are performed at the edge of the network with less delay and cost. Due to the energy limitation and low computational capacity of fog nodes, deciding to assign tasks to fog nodes is a critical issue. In this paper, a mathematical model for resource allocation with the aim of reducing delay and energy while considering Quality of Service (QoS) is proposed, and in the next step, a combined genetic and gray wolf algorithm is introduced to solve the model. The combination of these two algorithms provides helps to find an optimal solution in an efficient manner. It should be noted that the implementation of the mentioned algorithms has a processing cost and a computational delay, but due to the improvement of QoS, this cost can be ignored. Simulation results indicate that the combination and simultaneous use of the positive points of the two algorithms, the criteria of execution time and makespan of the tasks, as well as energy consumption compared to the semi-greedy method improves by 18.30%, 15.14% and 10.21%, respectively.