In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27\% compared to baseline algorithms.

翻译：在移动边缘计算（MEC）中，物联网（IoT）设备将计算密集型任务卸载至边缘节点，并在容器内执行，从而减少对集中式云基础设施的依赖。为保持边缘集群的高效安全运行，频繁升级至关重要。然而，传统云集群升级策略因边缘集群地理分布广且资源受限而难以适用。因此，合理调度容器并升级边缘集群以最小化对运行任务的影响至关重要。本文提出一种面向边缘集群升级的低延迟容器调度算法。具体包括：1）将边缘集群升级中的在线容器调度问题建模为最小化总任务延迟；2）提出一种基于策略梯度的强化学习算法，综合考虑MEC的独有特性来解决该问题；3）实验结果表明，与基线算法相比，本算法将总任务延迟降低约27%。