Kubernetes won the container orchestration wars. The CNCF reports that 82% of container users run Kubernetes in production. Despite its market dominance, Kubernetes teams aren’t always happy. It turns out that keeping developers and operations teams happy takes more than agreeing on which technology to use.

Developers appreciate Kubernetes self-service capabilities, which allow them to scale apps and tweak configurations without external approval. Operations teams often sweat the very same capabilities, or as our guest, HPE Fellow David Estes puts it, “operations teams have always wanted to slow things down,” as they favor stability, ticketing, and change requests. Developers, in contrast, “want to move fast.”

At the same time, apps are spreading across hybrid and edge computing environments as AI inference workloads soar, further stressing Kubernetes-using teams. Estes also thinks many companies lean too heavily on microservices, leading to “architecture bloat.”

So, what’s the fix? The good news is that while AI workloads are forcing companies to handle GPU scheduling and capacity scaling to handle demand spikes, AI automation may resolve some of the issues the technology is causing.

Estes reckons that developers will be able to use AI automation to monitor Kubernetes clusters, check logs, and perhaps “predict if there’s going to be an outage.” Operationally, using AI to automate some infra tasks could be “huge” for the Kubernetes landscape, the Fellow said.

HPE has created software to help resolve the Kubernetes issues we discuss – HPE OpsRamp to help manage multi-cloud and on-prem computing environments, for example – but beyond particular products, it’s encouraging to hear that AI itself may be able to remedy some of the new challenges its uptake has created.

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