Vela: A 3-Phase Distributed Scheduler for the Edge-Cloud Continuum

Abstract

The amalgamation of multiple Edge and Cloud clusters into an Edge-Cloud continuum requires efficient scheduling techniques to cope with high numbers of infrastructure nodes and computing jobs. Since monolithic schedulers typically do not scale well beyond a certain cluster size, distributed scheduling approaches are usually employed to address such scalability issues. Distributed schedulers are often designed for Cloud environments and lack support for the Edge. Conversely, many Edge schedulers focus on single clusters and provide limited support to deal with the scale of the Edge-Cloud continuum. In this paper, we present the Vela Distributed Scheduler, a globally distributed scheduler, which is specifically tailored for the Edge-Cloud continuum. The main contributions of our work include: i) A novel, globally distributed and orchestrator-independent scheduler with a 3-phase scheduling workflow; ii) A two-level, informed sampling mechanism, which reduces latency for globally distributed sampling and leverages job requirements to produce high quality node samples; And iii) a MultiBind mechanism that significantly reduces job evictions and rescheduling due to scheduling conflicts. We implement Vela on top of Kubernetes and evaluate it in a realistic large-scale setup using multiple interconnected, globally distributed, and production-ready MicroK8s clusters with up to 20,000 total simulated nodes. Our results show that Vela’s performance scales linearly with infrastructure size and that it reduces scheduling conflicts by a factor of 10.