Modeling Multi-constrained Fog-cloud Environment for Task Scheduling Problem

A fog-cloud system allows end devices to be integrated with computing resources, including fog equipment and cloud services in the IoT environment. Due to the heterogeneity and diversity of the components in a fog-cloud system, many factors such as computation, storage, latency, power consumption, and costs affect the operational effectiveness of that system. This paper deals with the problem of task scheduling in fog-cloud systems. Thus, we formulated a general model of the fog-cloud system with different constraints and then applied metaheuristic methods to schedule data processing tasks in this environment to solve a multi-objective optimization problem. Our simulated experiments proved that the proposed fog-cloud model could be applied to practical use cases. Also, the use of metaheuristic algorithms for the task scheduling problem performs well compared to the traditional methods like Round-Robin.