Volcano
Volcano is a free, self-hosted system for scheduling high-performance workloads on Kubernetes.
What Volcano is
Volcano is a free, self-hosted system for scheduling high-performance workloads on Kubernetes. It brings native Kubernetes workloads and batch jobs into one scheduling system and supports frameworks such as Spark, TensorFlow, PyTorch, Flink, Argo, MindSpore, PaddlePaddle, and Ray. Scheduling policies include gang, binpack, heterogeneous-device, capacity, fair-share, task-topology, and NUMA-aware options. Multi-level queues, quotas, borrowing, reclaiming, and preemption help manage resources across queues. Volcano can schedule across x86, Arm, Kunpeng, Ascend, GPU, and NPU resources, as well as support cross-cluster job scheduling for larger resource pools and load balancing. It also supports colocating online and offline workloads with dynamic resource overcommitment, CPU burst, and resource isolation. Logging, monitoring metrics, and a dashboard are described as operational tools. Installation options include YAML files, source code, and Helm on an existing Kubernetes cluster; Kubernetes 1.12 or later with CRD support is required. YAML installation supports x86_64 and arm64, while the documented one-click source installation is temporarily limited to x86_64. Volcano is a Cloud Native Computing Foundation incubating project.
Good
- Unifies native Kubernetes workloads and batch jobs
- Supports multiple scheduling policies
- Provides queues, quotas, borrowing, and preemption
- Supports cross-cluster job scheduling
- Free and self-hosted
Watch out
- Requires Kubernetes 1.12 or later with CRD support
- One-click source installation is temporarily limited to x86_64
- YAML installation supports only x86_64 and arm64
Who it suits
Volcano may suit teams running batch jobs or other high-performance workloads on Kubernetes, including teams managing diverse hardware or multiple clusters. It is self-hosted, so users need an existing Kubernetes cluster that meets the stated prerequisite.
Verdict
Volcano is a Kubernetes scheduling system with a broad set of workload, resource, and hardware options. Check its cluster prerequisite and installation architecture limits against your environment before deployment.
The facts
Each one read from Volcano's own pages, with the date it was checked.
What it does
- Architecture limit
- The documented one-click installation from source is temporarily available only for x86_64; YAML installation supports x86_64 and arm64.checked 5 Oct 2026
- Colocation
- Volcano supports colocating online and offline workloads using unified scheduling, dynamic resource overcommitment, CPU burst, and resource isolation.checked 5 Oct 2026
- Community support
- The project directs users to the CNCF #volcano Slack channel, a mailing list, and GitHub issues and repositories.checked 5 Oct 2026
- Deployment
- Installation options include YAML files, source code, and Helm on an existing Kubernetes cluster.checked 5 Oct 2026
- Frameworks
- It supports frameworks including Spark, TensorFlow, PyTorch, Flink, Argo, MindSpore, PaddlePaddle, and Ray.checked 5 Oct 2026
- Hardware
- It supports scheduling across x86, Arm, Kunpeng, Ascend, GPU, and NPU resources.checked 5 Oct 2026
- Monitoring
- The introduction describes logging, monitoring metrics, and a dashboard for graphical operations.checked 5 Oct 2026
- Multi-cluster
- It supports cross-cluster job scheduling for larger resource pools and load balancing.checked 5 Oct 2026
- Observability
- The project describes logging, monitoring metrics, and a dashboard for graphical operations.checked 5 Oct 2026
- Prerequisite
- The installation guide lists Kubernetes 1.12 or later with CRD support as a prerequisite.checked 5 Oct 2026
- Project status
- Volcano is a Cloud Native Computing Foundation incubating project.checked 5 Oct 2026
- Purpose
- Volcano is a cloud native system for high-performance workloads and a container batch scheduling project.checked 5 Oct 2026
- Queue management
- Volcano supports multi-level queues, multidimensional resource quotas, resource inheritance, borrowing, reclaiming, and preemption.checked 5 Oct 2026
- Resource management
- It provides multi-level queues, resource quotas, borrowing, reclaiming, and preemption between queues.checked 5 Oct 2026
- Scheduling policies
- Its scheduling policies include gang, binpack, heterogeneous-device, capacity, fair-share, task-topology, and NUMA-aware scheduling.checked 5 Oct 2026
- Supported architectures
- The YAML installation method is available for x86_64 and arm64, while installation from source is listed as temporarily available only for x86_64.checked 5 Oct 2026
- What it does
- Volcano is a cloud-native system for scheduling high-performance workloads on Kubernetes.checked 5 Oct 2026
- Workloads
- It schedules Kubernetes workloads and batch jobs for frameworks including Spark, TensorFlow, PyTorch, Flink, Argo, MindSpore, PaddlePaddle, and Ray.checked 5 Oct 2026
