Why Volcano
Unified Scheduling
Supports integrated job scheduling for both Kubernetes native workloads and mainstream computing frameworks (such as TensorFlow, Spark, PyTorch, Ray, Flink, etc.).
Queue Management
Provides multi-level queue management capabilities, enabling fine-grained resource quota control and task priority scheduling.
Heterogeneous Device Support
Efficiently schedules heterogeneous devices like GPU and NPU, fully unleashing hardware computing potential.
Network Topology Aware Scheduling
Greatly enhancing model training efficiency in AI distributed training scenarios.
Multi-cluster Scheduling
Supports cross cluster job scheduling, improving resource pool management capabilities and achieving large scale load balancing.
Online and Offline Workloads Colocation
Enables online and offline workloads colocation, improving cluster resource utilization through intelligent colocation scheduling.
Load Aware Descheduling
Optimizing cluster load distribution and enhancing system stability.
Multiple Scheduling Policies
Supports various scheduling strategies such as Gang scheduling, Fair-Share, Binpack, DeviceShare, NUMA-aware scheduling, Task Topology, etc.
