A retail company plans to migrate an on-premises Java application to Google Cloud. The application must load a proprietary kernel module at boot time and mount an NFS share configured in the instance's fstab file. Developers want to reuse the existing startup scripts and avoid containerizing or refactoring the code. Which Google Cloud compute option best meets these requirements while minimizing engineering effort?
Package the code in a container and deploy it to a GKE Autopilot cluster.
Rewrite the application as multiple Cloud Functions triggered by HTTP.
Containerize the application and deploy it to Cloud Run.
Create a custom image of the on-premises VM and run it in Compute Engine.
Compute Engine virtual machines provide root-level access and full control of the operating system. This allows you to run custom startup scripts, load kernel modules, and mount NFS volumes exactly as on-premises, enabling a straightforward lift-and-shift migration. Cloud Run and Cloud Functions are serverless platforms that do not let you modify the host OS or mount network file systems. GKE Autopilot manages the node operating system; containers cannot install kernel modules or rely on fstab for NFS mounts, so additional re-engineering would be required.
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What is Compute Engine, and why is it suitable for running a custom VM image?
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What is an NFS share, and why is it important for this use case?
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Why are options like Cloud Run and GKE Autopilot unsuitable for this migration?
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What is a custom image in Google Cloud Compute Engine?
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Why can't serverless platforms like Cloud Run modify the host OS?
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What is fstab and how does it relate to NFS mounts?
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GCP Associate Cloud Engineer
Planning and implementing a cloud solution
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