Energy-aware offloading based on priority in mobile cloud computing

Smartphones and portable devices have been widely used in our daily life. However, these portable devices cannot be used in a lot of environments due to limitations in battery capacity and computing speed. Therefore, how to save energy consumption and improve processing ability has become a hot issue. The previous works were mainly to transfer some local tasks to remote compute nodes by intelligently selecting the execution route of tasks based on Directed Acyclic Graph (DAG). Different from previous work, this work mainly attempts to consider task urgency and system load based on Dynamic Voltage and Frequency Scaling (DVFS) model. Firstly, tasks are given different priorities according to the energy consumption of different locations and task urgency. Secondly, a reference working state is given according to system load to ensure that each task (regardless of job deadline) is completed with minimum energy consumption under the average system load. Finally, a heuristic algorithm is used to schedule tasks on mobile devices and remote cloud resources based on the priority of tasks and reference status of resources. Our method is called AODVFS-Adaptive Offloading based on DVFS model. Comparison with other methods shows that our method saves average energy consumption and increases the number of completed tasks.