Novel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether call for a powerful resource provisioning platform that offers the applications the best of the underlying infrastructure. We therefore propose Kubernetes-edge-scheduler that provides high reliability for applications in the edge, while provisioning less than 10% of resources for this purpose, and at the same time, it guarantees compliance with the latency requirements that end users expect. We present a novel topology clustering method that considers application latency requirements, and enables scheduling applications even on a worldwide scale of edge clusters. We demonstrate that in a potential use case, a distributed stream analytics application, our orchestration system can reduce the job completion time to 40% of the baseline provided by the default Kubernetes scheduler.

新的应用程序需要扩展传统的云计算基础设施，并在靠近最终用户的地方部署计算资源。与运营商网络紧密集成的边缘和雾计算可以满足这一需求。重点在于集成：严格的延迟约束、确保分布式远程计算节点的可靠性以及系统的庞大规模共同要求一个强大的资源供应平台，为应用程序提供最好的底层基础设施。因此，我们提出了 Kubernetes-edge-scheduler，它为边缘的应用程序提供高可靠性，同时为此目的配置不到 10% 的资源，同时，它保证符合最终用户期望的延迟要求。我们提出了一种新颖的拓扑聚类方法，该方法考虑了应用程序延迟要求，并且甚至可以在全球范围的边缘集群上调度应用程序。我们证明，在一个潜在的用例中，分布式流分析应用程序，我们的编排系统可以将作业完成时间减少到默认 Kubernetes 调度程序提供的基线的 40%。