Your organization is containerizing a TensorFlow Serving-based model so that it can be horizontally scaled across an on-premises GPU cluster managed by Kubernetes. The model process keeps long-lived gRPC connections with upstream services, so each replica must keep a stable DNS hostname. The model also persists embeddings to disk and must have its own persistent volume that follows the replica if it is rescheduled. Finally, the team must perform zero-downtime rolling updates to deploy new model versions.
Which Kubernetes controller best satisfies all of these operational requirements?
A Kubernetes StatefulSet assigns each replica a predictable DNS hostname and its own persistent volume claim, giving every pod a stable network and storage identity even after rescheduling. The controller also supports ordered, partition-based rolling updates, allowing new model versions to be rolled out without downtime. A Deployment provides rolling updates but treats pods as interchangeable, so it cannot guarantee unique, stable identities or one-to-one volumes. A ReplicaSet underlies a Deployment but lacks native rolling-update or identity guarantees. A Job is designed for finite, batch workloads and does not keep long-running replicas. Therefore, only StatefulSet satisfies all three requirements (stable hostname, dedicated persistent volume, controlled rolling updates).
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Why is a StatefulSet better suited for handling stable DNS hostnames than a Deployment?
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How does a StatefulSet ensure each pod has its own dedicated persistent volume?
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What makes StatefulSet rolling updates zero-downtime compared to other Kubernetes controllers?