A data engineering team is preparing to deploy an internal stateful batch processing application to Google Kubernetes Engine (GKE). The application requires specific Linux kernel parameter modifications via sysctl settings on the underlying node OS to achieve optimal network socket throughput. Furthermore, the batch processing job cannot tolerate sudden node terminations or interruptions during its multi-hour execution window. Which deployment configuration should the cloud engineer implement to satisfy these technical requirements?
- Provision a GKE Standard cluster with a node pool composed of standard (on-demand) Compute Engine virtual machines, and configure the required sysctl kernel parameters on the node pool.Cevap
- BProvision a GKE Autopilot cluster and specify the custom sysctl kernel parameters directly inside the Pod deployment specification under securityContext.
- CProvision a GKE Standard cluster with a Spot VM node pool to minimize compute costs, relying on Kubernetes StatefulSet persistent volumes to recover from node preemptions.
- DProvision a GKE Autopilot cluster and execute `kubectl config set-cluster` to elevate cluster administrator privileges for modifying worker node OS settings.
Cevap
Provision a GKE Standard cluster using a standard on-demand node pool where sysctl kernel tuneables can be configured.
GKE Standard provides full operational control over node pools, enabling custom Linux sysctl kernel tuning needed for specific network performance profile requirements. Additionally, standard on-demand virtual machines ensure that long-running stateful batch workloads run without unexpected termination.
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GKE Standard vs Autopilot cluster capabilities and node pool VM instance suitability
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