DDPG-Based edge resource management for coal mine surveillance video analysis in cloudedge cooperation framework
DDPG-Based edge resource management for coal mine surveillance video analysis in cloudedge cooperation framework
复制标题
CloudEdge协作框架中基于DDPG的煤矿监控视频分析边缘资源管理
DOI：
10.1109/access.2021.3129465
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Jingzhao LI
中科院分区：
文献类型：
--
作者：
Zhi XU;Jingzhao LI
智能视频监控是保障煤矿安全生产的重要手段，而云端协同是提升智能视频监控性能的有效手段。然而，在边缘层中，计算资源和网络资源分配不当会造成资源浪费和实时性降低。提出了一种基于深度确定性策略梯度的云边缘协作框架的边缘资源分配方法。首先，针对不同的任务设计了云-边协作框架。其次，对边缘计算引起的延迟和带宽使用率联合最小化问题进行了建模。为了快速求解联合优化问题，我们将其转化为马尔可夫决策过程。此外，提出了边缘状态感知网络ESPN，增强了DDPG的特征感知能力和动作输出能力。最后，提出了用DDPG-ESPN来解决联合优化问题。仿真结果表明，与其他方法相比，DDPG-ESPN的实时性能和带宽利用率分别提高了18.88%和42.81%。
Intelligent video surveillance is important to ensure production safety in coal mines, while cloud-edge cooperation is an effective means to improve the performance of intelligent video monitoring. However, in edge layers, incorrect resource allocation of computing and network resources will result in the waste of resources and low real-time performance. In this paper, a DDPG-Based (Deep deterministic policy gradient-based) edge resource allocation method for cloud-edge cooperation framework is proposed. Firstly, the cloud-edge cooperation framework is designed for different tasks. Secondly, the joint minimizing problem of latency and bandwidth usage caused by edge computing is modeled. To quickly solve the joint optimization problem, we convert it to MDP (Markov Decision Process). In addition, ESPN (Edge status perception network) is proposed, which enhances the ability of feature perception and action output of DDPG. Finally, DDPG-ESPN is proposed to solve the joint optimization problem. Simulation results show that compared with other methods, DDPG-ESPN improves the real-time performance and bandwidth usage by up to 18.88% and 42.81% respectively.
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