An energy, delay and priority-aware task offloading algorithm for fog computing incorporating load balancing

Internet of things (IoT) and fog and cloud computing are the most widespread technologies that have become a part of our day-to-day activities. The integration of these technologies will become crucial components for the future of the Internet as their usage and acceptance rapidly increase. IoT devices (or terminal nodes (TNs)) have limited resource capability to carry out their generated tasks. Therefore, they depend on the cloud to assist them in completing all their tasks. However, the distance between TNs and the cloud may lead to network congestion and uneven delay. As a result, fog nodes (FNs) work as an intermediary between TNs and the cloud to minimize delay in completing the TNs' tasks. In this context, previous studies assign the TNs' tasks to FNs based on various criteria, namely energy, delay and priority among the tasks, without combining them. Fair task offloading (FTO) recently combines these criteria to assign the TN's tasks to FNs without significantly considering load balancing among FNs. This paper introduces a multi-objective task offloading algorithm called energy, delay and priority-aware task offloading (EDP-TO) by considering all the criteria and load balancing. The proposed algorithm uses the multi-objective function to select the FNs for offloading. It divides the tasks into multiple subtasks and assigns them to the chosen FNs, minimizing the overall delay. The performance of the proposed algorithm is shown without and with load balancing, called EDP-TO-WLB and EDP-TO-LB, and it is compared with FTO by considering three scenarios and five performance metrics. The comparison results show the EDP-TO improves a maximum of 3% energy, 5% delay and 45% fairness over the FTO.