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With the development of Internet of things (IoT) technology, fog computing has gradually come into people’s vision because of the characteristics of real-time and low delay. Providing computing and storage services at the edge of the network, fog computing can meet the needs of users in delay-sensitive applications as a highly virtualized platform. Resource scheduling plays an important role in fog computing, because it can meet the requirements of customers and improve the quality of service. However, if largescale service requests can not solve the resource scheduling problem effectively, it will reduce resource utilization and user satisfaction. Focus on the above problem, we establish a resource scheduling model named normalization processing, which can achieve the goal of the lowest total cost by finding the optimal pheromone. The optimal resource scheduling results can be obtained by changing the pheromone concentration of ants during the simulated foraging process. At the same time, we propose a new resource scheduling algorithm named new genetic ant colony optimization (NGACO) algorithm, which can update pheromone generation by roulette algorithm. The experimental results show that the resource scheduling performance of NGACO algorithm is 14.7%, 25%, 12.8% lower than that of ACO algorithm in makespan, economic cost, total cost respectively, and load balancing is 34.7% higher than ACO algorithm.

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