基于深度强化学习的边缘视频任务卸载策略

Edge video offloading strategy based on deep reinforcement learning

随着物联网的发展以及智能设备的普及,视频处理技术已广泛应用于生活中。自动驾驶、产品质检等应用场景对视频处理技术的实时性需求逐步提高,移动边缘计算为计算能力不足和能源受限的设备提供计算资源以执行时延敏感性任务,为实时视频处理提供了新的计算架构。本文搭建了一个视频计算卸载场景,并以视频检测为任务,以系统时延为优化目标,建立了计算卸载模型和马尔可夫决策模型;考虑到计算卸载场景的复杂动态因素,如带宽波动、设备数量、任务大小等,以最小化系统时延为目标,提出了一种基于深度强化学习的计算卸载策略进行求解。实验表明,与其他基线方案相比,该卸载策略能够适应较复杂卸载场景,有效降低系统时延。

With the development of the Internet of Things and the proliferation of smart devices,video processing technology has been widely applied in daily life.Applications such as autonomous driving and product quality inspection increasingly demand realtime video processing.Mobile edge computing provides computing resources for devices with limited computational power and energy,supporting time-sensitive tasks and offering a new computational architecture for real-time video processing.This paper constructs a video computation offloading scenario focused on video detection tasks,with system latency as the optimization objective.It establishes a computation offloading model and a Markov decision model.Considering the complex dynamic factors of the computation offloading scenario,such as bandwidth fluctuations,number of devices,and task size,a computation offloading strategy based on deep reinforcement learning is proposed to minimize system latency.Experiments show that this offloading strategy adapts well to complex offloading scenarios and effectively reduces system latency.