Introduction to Kueue
A cloud-native job queueing system for batch, HPC and AI/ML
I’ve been investigating Kueue to bring you a concise analysis that explains what it is and how it can be integrated into your platform.
☝🏼 Whether you’re managing AI tasks, large-scale data processing, or multi-user machine learning experiments, Kueue offers an intriguing approach to resource management and job scheduling in Kubernetes.
In today’s issue, we’ll cover:
🧐 What is Kueue?
⚙️ How does it work?
📌 Key use cases
👩💻 Is it production-ready?
🧐 What is Kueue?
Kueue is a Kubernetes-native system designed to manage and schedule jobs along with their resource quotas.
👉🏼 Its main purpose is to orchestrate resource-intensive workloads—think AI tasks, machine learning training, scientific simulations, and heavy data processing—while ensuring optimal utilization of limited resources (like GPUs or CPUs).
Key features include:
Resource Optimization: Kueue efficiently manages shared resources among different workloads. It helps schedule tasks that need exclusive access to finite resources, minimizing idle time and resource contention.
Seamless Kubernetes Integration: Operating with native Kubernetes objects, Kueue leverages Custom Resource Definitions (CRDs) to define queues and manage resource policies. It works harmoniously with BatchJobs, Kubeflow Training Jobs, and more.
Job Prioritization: You can enforce ordering and prioritization based on custom rules—deadlines, user importance, or specific resource requirements—to ensure that critical tasks aren’t delayed.
Observability & Insights: Built-in Prometheus metrics and condition reporting let you monitor the state of your jobs and the overall system, making it easier to debug and optimize your pipelines.
Advanced Autoscaling Support: With integration into the cluster-autoscaler’s provisioning request mechanism, Kueue can dynamically adjust to varying workloads.
For more in-depth details, you might also want to check out this extending Kueue across multi-clusters article.
⚙️ How Does It Work?
Let’s break it down in simple terms.
Imagine you’re running your Kubernetes cluster on AWS—perhaps using EKS. In this environment, your compute resources (CPU, memory, GPUs) come from EC2 or Fargate nodes. Kueue sits atop this infrastructure to ensure that jobs requiring these resources are scheduled intelligently.
Here’s the flow:
Job Submission: When you submit a job (via Kubernetes Job resources), you annotate it with metadata (like priority labels). Kueue then intercepts these submissions and queues them based on your defined policies.
Queue Management: Kueue maintains queues that order jobs by priority. It can decide which job to run next based on resource availability and your custom rules.
Resource Allocation: Once the required resources (such as GPUs or high-CPU nodes) become available, Kueue schedules the job for execution, ensuring critical workloads are handled promptly.
Monitoring & Autoscaling: Through its built-in metrics and integration with autoscaling tools, Kueue provides insights into job performance and resource utilization, allowing for dynamic adjustments.
☝🏼 I know; for us developers, this might be too much into infra knowledge. But that explanation from above is important for you, as a developer as well, so you can be more prepared to understand the use cases that match to use of this technology.
📌 Use Cases
Kueue is particularly useful in scenarios where resource contention is a concern:
GPU-Intensive Clusters: When managing clusters with limited GPU resources, Kueue can prioritize training jobs and simulations to ensure that high-priority tasks aren’t starved.
Data Processing Pipelines: For environments running large-scale queries or data transformations, Kueue can help orchestrate and prioritize jobs, ensuring efficient throughput.
Multi-User Machine Learning Experiments: In research or enterprise settings where multiple users submit training jobs simultaneously, Kueue’s prioritization can prevent resource hogging by less critical tasks.
In future emails, I will share with you how Kueue can be used more other use cases, oriented to executing queries in a prioritized manner.
👩💻 Is Kueue Ready for Production?
☝🏼 Every new technology begs the question: “Is it production-ready?”
I must confess, this is a technology I have not used yet. I’m exploring it (and this email comes from there) to simplify some use cases I have.
From my research and the aspects I use to take into account for the adoption of a new technology, here’s where things stand:
Community & Maturity: Kueue currently sits in the SANDBOX phase (the CNCF Sandbox is the entry point for early-stage projects) within the CNCF lifecycle. It has garnered around 1.5K stars on GitHub and attracted over 123 contributors. The community is active on Slack and through mailing lists (links are available in the GitHub repository).
Caution & Experimentation: While promising, Kueue is still in its early stages. I would say it’s an excellent candidate for non-critical parts of your system—areas where a slight risk is acceptable—but you should be cautious about deploying it in mission-critical environments until it matures further (moving from SANDBOX to INCUBATING or GRADUATED status).
Industry Interest: Notably, Google Cloud has shown interest by listing Kueue as a provider. This industry backing suggests that Kueue has potential, but it’s important to keep an eye on its evolution.
How does it sound to you?
Let’s wrap up for today.
✨ Takeaways
Experimentation Encouraged: Kueue offers a compelling approach to managing resource-intensive workloads. If your platform has a batch processing or non-critical path, it’s worth experimenting with Kueue.
Not a Silver Bullet Yet: Although Kueue presents an elegant solution to job scheduling in Kubernetes, its production readiness is still under evaluation. Keep testing and monitoring its development.
Stay Informed: As the project matures, expect more robust features, improved stability, and broader adoption. This is an area to watch closely if you’re dealing with complex, resource-constrained systems.
Kueue is an exciting step toward smarter, more efficient job scheduling in Kubernetes. By understanding its capabilities and limitations, you can better decide how and when it might fit into your platform.
I would love to read your thoughts about this technology; Do you know it already? What do you think? Reply to this email or send me one.
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