Mobile edge computing offload strategy based on energy aware
Kangkang Liao, Jun Jie Yang, Miao Li · 2022
Mobile edge computing (MEC) is an emerging paradigm to meet the growing computing needs from mobile applications. By offloading computing intensive mobile application tasks to MEC server, users can experience low latency, low energy consumption and high reliability services. However, due to the limited battery capacity on the device, task execution will be interrupted when the battery energy is exhausted. To solve the above problems, a task offloading strategy based on energy aware is proposed. In the scenario of multi-user and single server, the total queuing cost in the process of mobile device requesting service is analyzed by using queuing theory. Then, the mathematical model of the scene is established, and the linear weighting and objective function of time delay and energy consumption of mobile device are designed. In order to optimize the objective function and obtain the optimal solution, an improved genetic algorithm (Ga Energy Aware, GEA) is proposed. Simulation results show that the algorithm can effectively reduce the power consumption and task execution delay of mobile device.