Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Abstract

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.

Fault Tolerance in Real-Time Cloud Computing

“…Other, more recent, works [14], [30] base the fault tolerance mechanisms on containers, using Kubernetes to manage them and improve the Kubernetes scheduler and fault-detection mechanism to react to failures in a shorter time. However, they do not explicitly consider real-time applications and deadline constraints.…”

Cost-Effective Scheduling for Kubernetes in the Edge-to-Cloud Continuum

“…In [13], Toka presents a latency-aware scheduler that maximizes resource utilization at the edge. He also introduces a rescheduler that can improve application placement over time.…”