关键词: 移动边缘计算, 延时优化, 能耗优化, 近似计算, 遗传算法, 工作流

Abstract: The development of mobile devices has led to a proliferation of various computationally intensive mobile applications.In response to the prominent demand of such applications on the computational power and energy consumption of mobile devices,the workflow tasks were optimized with approximate computation,and the Quality of Service(QoS)perspective was introduced to limit the degree of optimization processing.An Energy Aware workflow offloading strategy based on QoS(EA-QoS)was proposed.In the EA-QoS strategy,the edge nodes was sorted according to the transmission energy consumption per unit load,the nodes were grouped based on the energy consumption threshold and the QoS levels were assigned,so as to reduce the complexity of subsequent offloading algorithms.The tasks were stratified and the Heterogeneous Earliest Finish Time(HEFT)algorithm was used to generate the ready queues.The Non-dominated Sorting Genetic Algorithm(NSGA-III)was used to find the optimal scheduling solution scheme by integer mapping of task execution order,edge nodes and QoS levels.The simulation experimental results showed that the EA-QoS policy improved the energy consumption optimization of workflows by an average of 12.02% and 35.14% compared to NSGA-III and PSO respectively.

Key words: mobile edge computing, delay optimization, energy optimization, approximate computation, genetic algorithms, workflow