A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

In cloud/fog-based environments, resource management is an important and challenging process. The deadline-based workflow scheduling mechanism is a common practice in such systems to overcome the complexities of resource management. However, many proposed approaches suffer from resource overloading/underloading, ignoring volunteer and volatile resources, and acting reactively. This paper presents a load-balancing method for IoT/Fog/Cloud environments integrated with local schedulers based on predicting workload and the presence of volatile mobile nodes (as dynamic resources). The proposed approach, firstly, turns the environment into a grid of equal-sized cells to reduce the system’s complexity. Then, the overall status of intra-cell resources (overloaded, underloaded, or normal) is estimated. This estimation is done according to the workload prediction and available dynamic resources. Finally, an exhaustive search is applied to dispatch extra workflows from an overloaded cell to an underloaded one in such a way as to avoid missing workflow deadlines and improve system performance. The proposed method is intended to be scalable and decentralized by nature, allowing it to be used in large-scale settings such as smart cities. Extensive software simulation is used to evaluate and compare the proposed method to with two recently published works. The simulation results show that the proposed method outperforms others regarding job completion rate, workload variances, and time-related parameters.