A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments

Abstract

In this paper, we propose a hybrid fog and cloud-aware heuristic for the dynamic scheduling of multiple real-time Internet of Things (IoT) workflows in a three-tiered architecture. In contrast to traditional approaches where the main processing of IoT jobs is performed in the fog layer, our approach attempts to schedule computationally demanding tasks with low communication requirements in the cloud and communication intensive tasks with low computational demands in the fog, utilizing possible gaps in the schedule of the fog and cloud virtual machines. Furthermore, during the scheduling process, our approach takes into account the communication cost incurred by the transfer of data from the sensors and devices in the IoT layer to the fog layer. The performance of the proposed heuristic is evaluated and compared via simulation to a baseline cloud-unaware strategy, under different cases of workload. The simulation results reveal that the proposed scheduling heuristic provides on average 76.69% lower deadline miss ratio, compared to the baseline policy. However, this is achieved at a significant monetary cost, due to the usage of cloud resources.