PGA: A Priority-aware Genetic Algorithm for Task Scheduling in Heterogeneous Fog-Cloud Computing

Abstract

Fog-Cloud computing has become a promising platform for executing Internet of Things (IoT) tasks with different requirements. Although the fog environment provides low latency due to its proximity to IoT devices, it suffers from resource constraints. This is vice versa for the cloud environment. Therefore, efficiently utilizing the fog-cloud resources for executing tasks offloaded from IoT devices is a fundamental issue. To cope with this, in this paper, we propose a novel scheduling algorithm in fog-cloud computing named PGA to optimize the multi-objective function that is a weighted sum of overall computation time, energy consumption, and percentage of deadline satisfied tasks (PDST). We take the different requirements of the tasks and the heterogeneous nature of the fog and cloud nodes. We propose a hybrid approach based on prioritizing tasks and a genetic algorithm to find a preferable computing node for each task. The extensive simulations evaluate our proposed algorithm to demonstrate its superiority over the state-of-the-art strategies.