Apache YuniKorn vs Volcano
Apache YuniKorn
yunikorn.apache.org
Volcano
volcano.shSide by side
| Figure | Apache YuniKorn | Volcano |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · GPU Cluster Management Software | 8.1 (#6) | 6.5 (#19) |
| Deployment | The project recommends deploying YuniKorn to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. | Installation options include YAML files, source code, and Helm on an existing Kubernetes cluster. |
| Purpose | Apache YuniKorn is a resource scheduler for running batch, data, and machine learning workloads on Kubernetes. | Volcano is a cloud native system for high-performance workloads and a container batch scheduling project. |
Plans
In their own words
Apache YuniKorn
Apache YuniKorn is a resource scheduler for batch, data, and machine learning workloads on Kubernetes. It is a Kubernetes-compatible alternative to the default scheduler and is described as transparent to existing Kubernetes applications. Hierarchical queues provide fine-grained tenant controls that can reflect an organization’s structure. Scheduling features include gang scheduling, FIFO and Fair job ordering, resource fairness, reservations, and preemption. A web UI centralizes cluster capacity, utilization, and application information. YuniKorn exposes metrics through Prometheus, and project guidance covers monitoring with Prometheus and Grafana. The project recommends deployment to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. The software is free under the Apache License 2.0. Releases include source tarballs, which must be built before use, and prebuilt Docker images. The project advises checking release artifacts for tampering with GPG or SHA-512. Help is available through the YuniKorn Slack #yunikorn-user channel or the user mailing list.