Choosing the best solution for running batch workloads on Kubernetes can be frustrating. Kueue, Volcano, and Apache YuniKorn were designed to address similar challenges but differ in how they tackle them. Deciding which is most suitable for a specific use case is often confusing.

Batch workloads like big data, data engineering, HPC, AI, and machine learning share common requirements, especially around batch-scheduling. Managing resource sharing and isolation between tenants while balancing utilization and meeting SLAs presents a significant challenge on Kubernetes.

This session dives into three community-driven solutions: Kueue, Volcano, and Apache YuniKorn. We’ll explore their features, use-case suitability, and design trade-offs, providing a comprehensive comparison. Attendees will leave with the insights needed to answer a crucial question: which solution best addresses the batch-scheduling needs of my workloads?