A financial analytics company is planning to deploy a high-performance quantitative risk simulation service on Google Cloud. The application runs as a custom containerized background process requiring custom Linux sysctl kernel parameters to optimize network socket buffers. The workload operates continuously 24/7, requires persistent block storage for local scratch caching, and cannot tolerate sudden instance preemption or termination. The cloud engineering team wants to select the most suitable compute platform while satisfying all operational requirements. Which compute strategy should the team choose?
- Deploy the workload on Google Kubernetes Engine (GKE) Standard node pools composed of standard Compute Engine virtual machines.Cevap
- BDeploy the workload on Compute Engine using Spot Virtual Machines attached to persistent disks to minimize compute costs.
- CDeploy the containerized workload on GKE Autopilot clusters to eliminate cluster node management overhead.
- DDeploy the workload as a container image on Cloud Run with persistent disk volume mounts enabled.
Cevap
Deploy the workload on Google Kubernetes Engine (GKE) Standard node pools composed of standard Compute Engine virtual machines.
Google Kubernetes Engine (GKE) Standard node pools using standard Compute Engine VMs offer the necessary flexibility to customize host OS settings—such as modifying Linux sysctl kernel parameters—while providing reliable, continuous 24/7 compute capacity without risk of abrupt preemption.
Adım Adım Çözüm
Anahtar Kavram
Planning Compute Infrastructure and Evaluating OS/Workload Constraints