基于深度确定性策略梯度的工业任务卸载策略

Industrial Task Offloading Strategy Based on Deep Deterministic Policy Gradient

工业互联网背景下的移动边缘计算(Mobile Edge Computing,MEC)通过在靠近终端的位置部署边缘服务器,将计算任务卸载到工业网络边缘,以满足任务实时响应和终端节能的需求。由于工业场景复杂性和环境动态性,卸载决策需要在满足任务时延需求的同时尽可能降低系统成本,为此提出了一个基于深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)的工业任务卸载策略。首先构建了一个端边协同的智能工厂MEC系统模型,以降低任务总时延和系统能耗为目标,将卸载问题转化为混合整数非线性规划问题,然后设计采用DDPG算法来得到最优卸载决策,提高服务质量,最大化节约系统成本。仿真结果表明,该策略在降低时延、系统能耗和成本方面比其他方法更优。

Mobile edge computing(MEC)in the Industrial Internet domain deploys edge servers near the terminals.It supports offloading the computation task into the industrial network edge to meet the requirements of real-time task response and terminal energy saving.Due to the complexity and dynamic nature of industrial scenarios,offloading decisions must satisfy the latency requirements of industrial applications while minimizing system costs.To tackle this challenge,we introduce an industrial task offloading strategy grounded in the deep deterministic policy gradient(DDPG)approach.This strategy aims to minimize total latency and energy consumption effectively.Firstly,an intelligent factory MEC system model for device-edge collaboration was constructed,and the offloading problem was formulated as a hybrid integer nonlinear programming;then,a DDPG algorithm was designed and proposed to optimize the objective function,further enhance the Quality of Service(QoS),and maximize the system’s cost savings.Simulation results demonstrate that our proposed DDPG-based strategy outperforms other approaches in reducing latency,energy consumption,and system cost.