A multi-objective fog computing task scheduling strategy based on ant colony algorithm

Abstract

Fog computing can effectively reduce latency and improve resource utilization by extending cloud services to the edge of the network. However, due to the wide variety of fog equipment and different computing capabilities, the theoretical knowledge and practical work related to fog computing task scheduling are insufficient. When scheduling tasks, factors such as cost of computing resources, power costs, and network cost were not considered comprehensively. Therefore, we propose a multi-objective fog computing task scheduling algorithm based on improved ant colony algorithm, which optimize the ant colony algorithm to make it more suitable for the characteristics of the fog node, use time and cost (TAC) to comprehensively consider the cost of the node, and introduce the critical factor in task allocation to improve the convergence speed of the algorithm. Different simulation experiments show that the efficiency of the improved ant colony algorithm is enhanced in processing time, cost, and load balance.