A global supply chain organization is architecting a new containerized route-optimization service on Google Cloud. The service receives stateless HTTP requests from mobile dispatch devices, executes short-lived algorithms requiring less than 15 seconds per request, experiences dramatic traffic fluctuations ranging from 0 to over 50,000 requests per minute during peak operational hours, and must scale down to zero during inactive periods to minimize costs. The organization enforces strict security policies prohibiting unmanaged server infrastructure, and the devops team aims to eliminate Kubernetes control plane management overhead while ensuring minimal cost for idle resources. Which compute platform design should you recommend?
- Deploy the containerized service to Cloud Run, configuring container concurrency and scaling limits to automatically scale instances down to zero when idle.Cevap
- BProvision a Google Kubernetes Engine (GKE) Standard regional cluster with Cluster Autoscaler and Horizontal Pod Autoscaler (HPA) enabled to manage the workload deployment.
- CDeploy the application onto a Compute Engine Managed Instance Group (MIG) behind an External HTTP(S) Load Balancer, configured to auto-scale strictly based on target CPU utilization.
- DPurchase 3-year Committed Use Discounts (CUDs) for fixed-capacity Compute Engine virtual machine instances to host the container runtime environments continuously.
Cevap
Deploy the containerized service to Cloud Run with automatic scaling down to zero when idle.
Cloud Run is the optimal Google Cloud platform for containerized, stateless HTTP workloads with bursty request patterns. It abstracts away all server infrastructure and cluster management while providing seamless automatic scaling from zero to tens of thousands of requests per minute, charging strictly for resources consumed during request execution.
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Selecting Serverless Container Compute Platforms based on Workload Characteristics