Edge computing has garnered significant attention in recent years, leading to the evolution of more delay-sensitive applications towards a three-tier architecture with edge-cloud collaboration. Concurrently, technologies associated with containerization have been maturing. Notably, (Kubernetes) emerges as a prominent solution for the management of extensive, dynamically evolving, and intricate container clusters. However, optimizing performance in a K8s-based architecture requires careful consideration, as intelligent algorithms cannot be easily rehearsed or retracted, and a poorly functioning algorithm can result in significant damage. To enhance the success rate of time-critical task execution in real production environments, it is crucial to provide an algorithm optimizer for service orchestration and request dispatching, especially when dealing with different services. Traditionally, building a K8s-based experimental system during the early stages of experiments has been time-consuming and involved significant programming efforts. In this paper, we introduce , a decoupled and modularized optimizer for scheduling in multiple clusters. also serves as an online testbed for algorithm verification in -based systems, offering detailed configuration options to facilitate cluster management. By collecting system information and employing an interface-oriented system architecture, enables users to quickly develop, deploy, and switch between various algorithms, significantly reducing the upfront costs associated with setting up an experimental environment. We evaluated the performance of based on overall overload and demonstrated its scalability and effectiveness in verifying the efficacy of service orchestration and request dispatching algorithms for time-critical tasks. Our findings illustrate the value of in improving the overall success rate of executing time-critical tasks, thus highlighting its potential in real-world scenarios.

中文翻译：

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EdgeOptimizer：基于 Kubernetes 的边缘云集群中时间关键任务的可编程容器化调度程序

近年来，边缘计算引起了广泛关注，导致对延迟更加敏感的应用程序向具有边缘云协作的三层架构发展。与此同时，与容器化相关的技术已经日趋成熟。值得注意的是，（Kubernetes）已成为管理广泛、动态发展且复杂的容器集群的杰出解决方案。然而，在基于 K8s 的架构中优化性能需要仔细考虑，因为智能算法无法轻易排练或撤消，而功能不佳的算法可能会导致重大损害。为了提高实际生产环境中时间关键任务执行的成功率，提供用于服务编排和请求调度的算法优化器至关重要，特别是在处理不同的服务时。传统上，在实验的早期阶段构建基于 K8s 的实验系统非常耗时，并且需要大量的编程工作。在本文中，我们介绍了一种用于多集群调度的解耦和模块化优化器。还充当基于系统的算法验证的在线测试台，提供详细的配置选项以促进集群管理。通过收集系统信息并采用面向接口的系统架构，使用户能够快速开发、部署和切换各种算法，从而显着降低与设置实验环境相关的前期成本。我们根据整体过载评估了性能，并在验证时间关键任务的服务编排和请求调度算法的有效性方面展示了其可扩展性和有效性。我们的研究结果说明了提高执行时间关键任务的总体成功率的价值，从而凸显了其在现实场景中的潜力。