Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm

As the amount of data input increases, fog devices on IoT edge networks become increasingly inefficient. However, a well-designed computing resource-scheduling strategy can help to reduce excessive time delays and energy consumption. Therefore, in this paper, we propose an efficient strategy. First, used particle swarm optimization (PSO) determine optimal load balance among nodes obtain computation consumption single cluster. Second, designed genetic joint artificial bee colony algorithm (PGABC) optimize task scheduling clusters based obtained from balancing. In addition, PGABC was task-scheduling model, which further reduced delay computing. The experimental results show that calculated using proposed given model by 1.04%, 15.9%, 28.5%, compared GABC, ABC, PSO, respectively, 3.9%, 6.6%, 12.6%, respectively. approximately 31.25%, 27.8%, 25.4%, 9.7%, 33.3%, 32%, 29.6%, SJF–PSO, PGABC-R, HSF.ABC&PSO, MFO,