Professional Cloud ArchitectDesign and plan a cloud solution architectureMedium
A research institution processes highly sensitive genomic data and uses custom-built, open-source tools for analysis. These tools are containerized and require access to GPUs for accelerated processing. The institution needs to run these jobs in a secure, isolated environment, with minimal operational overhead for managing the underlying infrastructure. They also need to ensure that the compute resources are provisioned on-demand and scale automatically. Which Google Cloud service should they choose?
- ACompute Engine with GPU-enabled VMs
- BCloud Run for Anthos
- CGoogle Kubernetes Engine (GKE) Standard
- DGKE Autopilot
Show answer & explanationAnswer & explanation
Correct answer: D. GKE Autopilot
GKE Autopilot provides a fully managed Kubernetes experience, handling node provisioning, scaling, and upgrades with minimal operational overhead. It supports GPU workloads and offers a secure, isolated environment for containerized applications, perfectly matching the research institution's requirements.
Why the other options are wrong
- A. Compute Engine provides VMs with GPUs, but requires significant operational overhead for managing Kubernetes, scaling, and ensuring security policies for containers, which the institution wants to minimize.
- B. Cloud Run for Anthos is for serverless containers on Anthos, but is typically geared towards HTTP-triggered services and might not be the most direct fit for batch-like genomic processing with GPUs compared to a fully managed GKE with Autopilot.
- C. GKE Standard requires manual management of nodes and scaling, which increases operational overhead compared to Autopilot, and doesn't fully meet the 'minimal operational overhead' requirement.
GKE Autopilot
A fully managed mode of Google Kubernetes Engine (GKE) where Google manages the cluster's underlying infrastructure, including nodes, scaling, and upgrades.
- Minimizes operational overhead for Kubernetes clusters.
- Automatically provisions and scales nodes based on workload.
- Supports various workloads, including those requiring GPUs.
Memory trick: For containers with GPUs, Autopilot takes the wheel, so you can focus on the code.