A company has developed a custom containerized API packaged as a standard OCI container image. The workload exhibits highly sporadic and unpredictable traffic patterns throughout the day, often experiencing several consecutive hours of zero incoming traffic. The architecture team has established the following operational and economic requirements: Scale-to-zero: Eliminate all compute infrastructure costs during periods of inactivity. Cold start mitigation: Reduce latency during initial container startup when scaling up from zero. Resource cost optimization: Pay for CPU resources only during active request processing rather than continuously. Container portability: Run the unmodified container image without rewriting application code into vendor-specific function wrappers. Which architecture should the cloud architect recommend?

Deploy the container to Cloud Run with CPU allocated only during request processing and enable Startup CPU Boost: ### What Cloud Run with Startup CPU Boost and CPU Idle is **Cloud Run** is a fully managed serverless compute platform that runs stateless **OCI containers** directly on Google Cloud infrastructure. It provides native **scale-to-zero** capabilities, automatically terminating container instances when there are no inbound HTTP requests and dynamically instantiating new instances as demand arrives. ### How it meets the requirements - **Scale-to-zero:** Cloud Run automatically scales down to zero active instances when traffic drops, ensuring zero compute and memory billing during idle periods. - **Cold start mitigation:** Enabling **Startup CPU Boost** temporarily allocates additional CPU resources (typically doubling vCPU capacity during container initialization) to shorten execution time during instance startup before scaling back to normal CPU limits for active requests. - **Resource cost optimization:** Configuring CPU to be allocated only during request processing (`cpuIdle: true`) ensures that billable vCPU seconds are tracked exclusively while handling active requests rather than charging for idle container background time. - **Container portability:** Cloud Run supports standard OCI container images without requiring proprietary SDKs, runtime modifications, or platform-specific function wrappers. ### Key advantages - **Automatic horizontal scaling** that instantly adjusts from 0 to hundreds of instances based on incoming request volume. - **Cost-effective pay-per-use model** billed in sub-second increments based on resource allocation during request lifecycles. - **Seamless CI/CD integration** supporting standard container workflows and open standards. - **Reduced operational complexity** with built-in HTTPS endpoints, TLS certificate provisioning, and traffic routing. ### Why this is optimal This approach delivers the exact balance between **scale-to-zero cost efficiency**, **fast startup performance**, and **full container portability** without the management overhead of underlying cluster nodes or virtual machines.

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Cloud Run Scale To Zero Startup Boost

Section: Mapping compute needs to platform products (e.g., Google Kubernetes Engine [GKE], Cloud Run, and Cloud Run functions)

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A company has developed a custom containerized API packaged as a standard OCI container image. The workload exhibits highly sporadic and unpredictable traffic patterns throughout the day, often experiencing several consecutive hours of zero incoming traffic.

The architecture team has established the following operational and economic requirements:

Scale-to-zero: Eliminate all compute infrastructure costs during periods of inactivity.
Cold start mitigation: Reduce latency during initial container startup when scaling up from zero.
Resource cost optimization: Pay for CPU resources only during active request processing rather than continuously.
Container portability: Run the unmodified container image without rewriting application code into vendor-specific function wrappers.

Which architecture should the cloud architect recommend?

Deploy the container to a Google Kubernetes Engine (GKE) Autopilot cluster and configure the Horizontal Pod Autoscaler (HPA) to scale deployment replicas to zero

Refactor the service logic into Cloud Run functions triggered by HTTP requests with minimum instances set to zero

Deploy the container using Compute Engine managed instance groups (MIGs) with schedule-based autoscaling

Deploy the container to Cloud Run with CPU allocated only during request processing and enable Startup CPU Boost

Professional Cloud Architect

Cloud Run Scale To Zero Startup Boost

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