基于深度强化学习的改进移动边缘计算任务卸载算法研究

Research on Improved Moving Edge Computing Task Unloading Algorithm Based on Deep Reinforcement Learning

大数据时代下移动终端用户规模不断扩大,万物互联在给人们带来极大便利的同时,也存在大量数据地理位置分散的问题,给用户服务质量QoS带来了极大挑战。首先,搭建一个基于移动边缘计算平台三层服务架构的任务卸载模型。其次,结合MEC平台实际应用场景,利用同策略经验回放和熵正则改进深度强化学习算法,优化了MEC平台的任务卸载策略,并设计了实验对3种传统算法和改进算法的能耗、时延、网络使用量进行对比分析。实验结果表明,改进算法在降低能耗、时延和网络使用量方面具有更优越的性能。

In the era of big data,the scale of mobile terminal users continues to expand,and the Internet of everything brings great convenience to people.At the same time,there is also the problem of geographic dispersion of a large amount of data,which brings great challenges to the QoS of user service.In this paper,a task unloading model based on the three-layer service architecture of the mobile edge computing platform is first built.Combined with the actual application scenario of the MEC platform,the deep reinforcement learning algorithm is improved by using the same policy experience playback and entropy regularization,and the task unloading strategy of the MEC platform is optimized.Experiments are designed to compare and analyze the three indexes of energy consumption,delay and network usage of the three traditional algorithms and the improved algorithm,and verify that the improved algorithm has better performance in reducing energy consumption,delay and network usage.