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AI jobs are waiting while GPUs sit idle. Kubernetes has a new plan.

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AI jobs are waiting while GPUs sit idle. Kubernetes has a new plan.

Welcome to another edition of Road to KubeCon! We’re tracking movements in the Kubernetes and cloud-native ecosystem as we count down to KubeCon + CloudNativeCon North America 2026 November 9–12 in Salt Lake City.

Half of our column this week focuses on untangling Kubernetes as we add the demands of agentic AI: automating pod-restart troubleshooting, syncing communication between the scheduler and autoscaler, and maturing how we distribute inference across a cloud-native stack.

The other half goes deeper into what we need to look at closely in Kubernetes environments: What context’s missing, what slips through the gaps in metrics, and whether the AI agents you already use are really worth their salt as SREs.

As more enterprises turn to Kubernetes for large-scale AI and machine learning jobs, they’re hitting a common obstacle: pod restarts. This is something that HPE has noticed as customers utilize its HPE Private Cloud AI (PCAI) to host long-running AI/ML workloads on inherently ephemeral container-based infrastructure like Kubernetes.

Hewlett Packard Enterprise (HPE) is a presenting sponsor of Road to KubeCon. HPE Software helps IT organizations modernize infrastructure, streamline operations, and accelerate AI initiatives across hybrid, multi-vendor environments.

According to HPE system software engineer Guoping Jia, Kubernetes provides some troubleshooting commands out of the box, but it still requires extensive manual investigation. “This operational workflow does not scale effectively in PCAI’s large-cluster environment,” writes Jia on the HPE blog.

His solution involves smarter automation for troubleshooting, using an open-source Kubernetes troubleshooting tool, k8s-pod-restart-info-collector , to automatically gather details and send diagnostic information straight to a Slack channel.


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