Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and show through a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

基于容器的虚拟化是雾和边缘计算应用程序中一种有前途的部署模型，因为它允许虚拟化应用程序在异构环境中无缝共存，而不会引入大量开销。某些应用领域（例如工业自动化、汽车或航空航天）要求应用程序表现出一定程度的时间可预测性。基于容器的虚拟化不能轻松地用于此类应用程序，因为该技术并非旨在支持实时属性和处理时间干扰。本文提出了一个由静态离线和动态在线阶段组成的框架，用于实时容器的资源分配和自适应重新尺寸调整。在离线阶段，根据理想的系统模型确定容器的最佳初始部署和规模。此外，为了适应工作负载变化或干扰引起的动态变化，在线阶段会在运行时调整实时容器的CPU使用率和限制，以改善实时容器化应用程序的实时行为，同时优化资源使用。我们在真实的基于 Linux 的系统中实现了该框架，并通过一系列实验表明，所提出的框架能够在容器之间调整和重新分配计算资源，以提高容器化应用程序在存在时间干扰的情况下的实时行为同时优化资源利用。