Uploaded August 2026 | Updated September 2026, 3 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
Score-Driven Multi-Cluster Management: An Evaluation Framework for Decision-Making - Kazuma Takeuchi, SoftBank Corp. & Joydeep Banerjee, Red Hat
As large AI platforms scale across multiple clusters, static placement policies are reaching their limits. Decisions must reflect live conditions such as GPU utilization, power efficiency, and other operational metrics rather than fixed rules alone.
In this talk, we introduce the Dynamic Scoring Framework, a new Add-on for Open Cluster Management that brings real-time telemetry into multi-cluster placement and policy decisions. Lightweight agents collect metrics from sources such as Prometheus, evaluate them through modular scoring APIs, and feed results into the central hub. This hybrid design balances distributed scoring and centralized control for scalable, flexible decision-making.
Through an architecture deep dive and a demo of resource optimization with the framework, we show how score-based decisions improve resource efficiency in AI infrastructure. Attendees will learn score-based management patterns and how to apply them beyond AI workloads.
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
Score-Driven Multi-Cluster Management: An Evaluation Framework for Decision-Making - Kazuma Takeuchi, SoftBank Corp. & Joydeep Banerjee, Red Hat
As large AI platforms scale across multiple clusters, static placement policies are reaching their limits. Decisions must reflect live conditions such as GPU utilization, power efficiency, and other operational metrics rather than fixed rules alone.
In this talk, we introduce the Dynamic Scoring Framework, a new Add-on for Open Cluster Management that brings real-time telemetry into multi-cluster placement and policy decisions. Lightweight agents collect metrics from sources such as Prometheus, evaluate them through modular scoring APIs, and feed results into the central hub. This hybrid design balances distributed scoring and centralized control for scalable, flexible decision-making.
Through an architecture deep dive and a demo of resource optimization with the framework, we show how score-based decisions improve resource efficiency in AI infrastructure. Attendees will learn score-based management patterns and how to apply them beyond AI workloads.
