Industries are considering the adoption of cloud computing for real-time applications due to current improvements in network latencies and the advent of Fog and Edge computing. To create an RT-cloud capable of hosting real-time applications, it is increasingly significant to improve the entire stack, including the containerization of applications, and their deployment and orchestration across nodes. However, state-of-the-art orchestrators (e.g., Kubernetes) and underlying container engines are designed for general-purpose applications. They ignore orchestration and management of shared resources (e.g. memory bandwidth, cache, shared interconnect) making them unsuitable for use with an RT-cloud. Taking inspiration from existing resource management architectures for multicore nodes, such as ACTORS, and for distributed mixed-criticality systems, such as the DREAMS, we propose a series of extensions in the way shared resources are orchestrated by Kubernetes and managed by the underlying Linux layers. Our approach allows fine-grained monitoring and allocation of low-level shared resources on nodes to provide better isolation to real-time containers and supports dynamic orchestration and balancing of containers across the nodes based on the availability and demand of shared resources.
翻译:由于目前网络延迟和雾与边缘计算机的出现,工业正在考虑采用云计算实时应用的云计算,因为目前网络延迟和雾与边缘计算机的出现,为了创建一个能够托管实时应用程序的RT-球云,越来越需要改进整个堆叠,包括应用程序的集装箱化及其在节点的部署和交响,然而,最先进的管弦乐器(例如Kubernetes)和基本集装箱引擎是为普通用途应用程序设计的,它们忽视了对共享资源(例如记忆带宽、缓存、共享连接)的管弦和管理,使它们不适合用RT-球云来使用;从现有的多核心节点资源管理结构(例如ACTORS)以及分布式混合临界系统(例如DREAMS)中得到灵感,我们提议了一系列延长,即由Kubernetes来安排共享资源,并由基本的Linux层管理。我们的方法允许对节点上的低水平共享资源(例如记忆带宽、缓存、共享连接、共享连接)进行精细的监测和分配,以便更好地隔离实时容器,并支持在不共享的基础上共享资源的基础上共享资源。