Compare Custom Schedulers for Kubernetes¶
In the previous blog, we introduced the concept of custom schedulers and why they are necessary for certain use cases. In this blog, we will compare and contrast three popular schedulers: Volcano, Kueue and Yunikorn.
Detailed Comparison¶
Volcano, Kueue, and YuniKorn are orchestration frameworks designed to handle batch or long-running jobs. Each offers unique capabilities that are tailored for specific use cases. Let us look at this in detail.
Volcano¶
Volcano is designed for high-performance, high-throughput computing (HPC), AI, ML, and other large-scale, batch computing workloads.
Key Features¶
- Supports MPI (Message Passing Interface) jobs, which are common in distributed computing.
- Built-in scheduling for batch workloads, ensuring fairness and priority for jobs.
- Offers advanced job lifecycle management (preemption, backfilling, gang scheduling).
- Can integrate with custom job controllers and other Kubernetes scheduling plugins.
Use Case¶
Volcano is ideal for complex HPC workloads, AI/ML model training, or other distributed computing tasks where job dependency and high scheduling precision are important.
Kueue¶
Kueue is a Kubernetes-native queuing system designed to manage batch jobs and workloads more efficiently by introducing an abstraction layer between jobs and cluster resources.
Key Features¶
- Provides a lightweight queuing system for batch jobs.
- Helps with better resource allocation, allowing you to run more jobs with limited resources.
- Can be integrated with different scheduling systems, making it versatile.
- Works with Kubernetes Job API, providing familiar interfaces.
Use Cases¶
Kueue is best suited for teams looking for a simple, Kubernetes-native solution to better handle resource allocation and fairness for batch jobs without requiring complex HPC-level capabilities.
YuniKorn¶
YuniKorn is a resource scheduler that can manage workloads in both Kubernetes and Apache Hadoop YARN environments. It is designed to handle large-scale resource scheduling for batch jobs and long-running services.
Key Features¶
- Supports fine-grained resource allocation (CPU, memory, GPU).
- Provides hierarchical queues and preemption, which ensures fairness among jobs and users.
- Can schedule both batch and long-running jobs, making it more versatile.
- Integrates with both Kubernetes and YARN, making it suitable for hybrid environments.
Use Cases¶
YuniKorn is useful in organizations that need to manage both Kubernetes and YARN clusters or have a mix of batch and long-running jobs with complex scheduling requirements.
Summary¶
In this blog, we compared three popular custom schedulers for Kubernetes and their suitability for various use cases.
- Volcano is best for high-performance and large-scale distributed jobs (e.g., AI/ML or HPC).
- Kueue offers a lightweight, Kubernetes-native solution to manage batch job resource allocation.
- YuniKorn provides more comprehensive resource scheduling and is especially valuable for hybrid Kubernetes/YARN environments or environments with diverse job types.
Thanks to readers of our blog who spend time reading our product blogs and suggest ideas.
Important
Rafay's Ray as a Service offering for AI/ML uses the Volcano custom scheduler for Kubernetes.
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