In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23\% and 35\% on average in terms of total delay and waiting delay, respectively.

翻译：在边缘计算中，容器被越来越多地用于部署应用，为移动用户提供服务。每个容器必须基于本地存在的容器镜像文件运行。然而，现有研究明显忽视了这一点：结合动态容器镜像缓存的有效任务调度，是降低边缘节点有限带宽资源下容器镜像下载时间的一种有前景的方法。为填补这一空白，本文提出了新颖的联合任务调度与镜像缓存（TSIC）算法，具体包括：1）我们将联合任务调度与镜像缓存问题建模为马尔可夫决策过程（MDP），综合考虑通信延迟、等待延迟和计算延迟；2）为求解该MDP问题，提出了一种基于深度强化学习的TSIC算法，该算法具有定制的状态空间和动作空间，并结合了自适应缓存更新算法；3）实现了一个真实的容器系统以验证我们的算法。实验表明，我们的策略在总延迟和等待延迟方面，平均分别优于现有基线方法23%和35%。