Energy-efficient scheduling based on task prioritization in mobile fog computing

Mobile network processing and the Edge Computing paradigm can be integrated as a unit called mobile fog computing in fifth-generation networks. Because mobile devices have less computing capacity such as limited CPU power, storage capacity, memory constraints, and limited battery life, therefore, computationally intensive tasks migrate from MDs to MFC. In this paper, we formulate an optimization scheme based on the Greedy Knapsack Offloading Algorithm (GKOA) to minimize the energy consumption of the MDs and save the capacity of limited resources. For resource allocation and dynamic scheduling, we present a dynamic scheduling algorithm based on the priority queue. We design two queues where the tasks with high execution times have the high priority in high time queue and the other, tasks with low execution times have the high priority in low time queue. These two priority queues work together and call as High Low Priority Scheduling (HLPS) model. Numerical results demonstrate the GKOA scheme improves energy efficiency by 19%, system overhead by 13.87%, and average delay by 8.5% on the MD side than local computing. Also, our proposed scheduling algorithm performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, average response time and the number of scheduled tasks on the MFC side.