Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of jobs. Most of the existing state-of-the-art focused on building new efficient scheduling algorithms for placing the containers on the infrastructure, and the resources to the containers are allocated manually and statically. An automatic method to identify and allocate optimal CPU resources to the containers can help to improve the efficiency of the scheduling algorithms. In this article, we introduce a new deep learning-based approach to allocate optimal CPU resources to the containers automatically. Our approach uses the law of diminishing marginal returns to determine the optimal number of CPU pins for containers to gain maximum performance while maximizing the number of concurrent jobs. The proposed method is evaluated using real workloads on a Docker-based containerized infrastructure. The results demonstrate the effectiveness of the proposed solution in reducing the completion time of the jobs by 23% to 74% compared to commonly used static CPU allocation methods.

中文翻译：

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容器的自适应 CPU 资源分配的递减回报和深度学习

容器为微服务应用程序提供了一个轻量级的运行环境，同时实现了更好的服务器利用率。将 CPU 引脚自动优化分配到服务于特定工作负载的容器有助于最大限度地缩短作业的完成时间。大多数现有的最新技术都专注于构建新的高效调度算法，用于将容器放置在基础设施上，并且容器的资源是手动和静态分配的。一种自动识别最佳 CPU 资源并将其分配给容器的方法有助于提高调度算法的效率。在本文中，我们介绍了一种新的基于深度学习的方法来自动为容器分配最佳 CPU 资源。我们的方法使用边际收益递减规律来确定容器的最佳 CPU 引脚数，以在最大化并发作业数量的同时获得最大性能。所提出的方法是使用基于 Docker 的容器化基础设施上的真实工作负载来评估的。结果表明，与常用的静态 CPU 分配方法相比，所提出的解决方案在将作业的完成时间减少 23% 到 74% 方面是有效的。