Nowadays, Internet of Things (IoT) devices are ubiquitous and their number is growing rapidly. These devices produce massive amount of data which need to be efficiently processed. Since most of the IoT devices are resource constrained in terms of computational capability and power resources, they have to offload their computation jobs to more powerful computing devices. Fog–cloud computing is a promising platform for processing IoT jobs. However, due to the heterogeneity of the computing devices, how to schedule IoT jobs in this environment is a challenging issue. To tackle this issue, in this paper, we first present a system model for the job scheduling problem in fog–cloud computing with the aim of optimizing the total deadline violation time of jobs and the energy consumption of the system. Then, we propose two nature-inspired optimization techniques, grey wolf optimization and grasshopper optimization algorithm to efficiently solve the job scheduling problem in the fog–cloud environment. The performance of the proposed algorithms is evaluated against the state-of-the-art algorithms using various simulation experiments. The results demonstrate that the proposed schedulers are capable of reducing the total deadline violation time about 68% and energy consumption about 22% compared to the second-best results.
