Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. This paper considers the scenarios of AI-based IoT applications based on container technology in a cloud-edge collaborative environment, and proposes a container-based task scheduling algorithm. Using priority-aware greedy strategy, a new scheduling algorithm named PGT has been proposed which adopts the multi-criteria approach Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The containers in cloud servers and edge servers are managed in a uniform platform, and IoT application services are deployed in containers. The task with smaller deadline constraint is scheduled first due to its higher priority. Then, multiple indicators are considered comprehensively, such as task response time, energy consumption, task execution cost, to find the optimal container to execute task. Through varying the number of edge servers and the number of tasks, the simulation results in a cloud-edge collaborative environment indicate that the proposed scheduling approach outperforms the four baseline algorithms in improving QoS satisfaction rate, energy consumption, penalty cost and total violation time.

以 Docker 为代表的容器虚拟化技术以其轻量级、快速部署、易移植等优势在业界得到广泛应用。本文考虑云边协同环境下基于容器技术的人工智能物联网应用场景，提出一种基于容器的任务调度算法。使用优先级感知贪心策略，提出了一种新的调度算法PGT，该算法采用多准则方法通过与理想解相似度排序优先技术（TOPSIS）。云服务器和边缘服务器中的容器在统一的平台上进行管理，物联网应用服务部署在容器中。由于具有较高的优先级，具有较小期限约束的任务首先被调度。然后，综合考虑任务响应时间、能耗、任务执行成本等多项指标，找到执行任务的最优容器。通过改变边缘服务器的数量和任务的数量，在云边缘协作环境中的仿真结果表明，所提出的调度方法在提高 QoS 满足率、能耗、惩罚成本和总违规时间方面优于四种基线算法。