Uploaded August 2026 | Updated September 2026, 2 weeks ago
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Kube-scheduler-evaluator: Evaluating Kubernetes Schedulers at Hyperscaler Scale in Seconds - Rihito Bannai, Keio University & Hidehito Yabuuchi, Preferred Networks, Inc.
As AI/ML workloads grow in importance on Kubernetes, evaluating schedulers at scale is critical yet costly. Running tens of thousands of real jobs across thousands of nodes is expensive, and training jobs can take days — making real-time evaluation impractical. Existing tools fall short: scheduler_perf is limited to micro-benchmarks, and kwok/kind still run in real time.
We developed kube-scheduler-evaluator, an open-source framework with three key capabilities:
Go-Based Scenarios: Define large-scale workloads programmatically using Go, not static YAML.
Virtual Time: Decouple simulation from wall-clock time — a multi-day cluster trace with thousands of GPU jobs can complete in seconds.
Flexible Execution: Run as a single binary with emulated controllers, or connect to kwok, kind, or a real cluster.
Attendees will learn how to evaluate schedulers using kube-scheduler-evaluator and how to apply the results to accelerate scheduler development.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Kube-scheduler-evaluator: Evaluating Kubernetes Schedulers at Hyperscaler Scale in Seconds - Rihito Bannai, Keio University & Hidehito Yabuuchi, Preferred Networks, Inc.
As AI/ML workloads grow in importance on Kubernetes, evaluating schedulers at scale is critical yet costly. Running tens of thousands of real jobs across thousands of nodes is expensive, and training jobs can take days — making real-time evaluation impractical. Existing tools fall short: scheduler_perf is limited to micro-benchmarks, and kwok/kind still run in real time.
We developed kube-scheduler-evaluator, an open-source framework with three key capabilities:
Go-Based Scenarios: Define large-scale workloads programmatically using Go, not static YAML.
Virtual Time: Decouple simulation from wall-clock time — a multi-day cluster trace with thousands of GPU jobs can complete in seconds.
Flexible Execution: Run as a single binary with emulated controllers, or connect to kwok, kind, or a real cluster.
Attendees will learn how to evaluate schedulers using kube-scheduler-evaluator and how to apply the results to accelerate scheduler development.
