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Difficulty: HardDesigning Infrastructure for Business Requirements and Cost Optimization

A global retail enterprise is re-architecting its e-commerce web platform on Google Cloud. The application experiences steady baseline usage throughout the day, but experiences unpredictable flash sales where traffic surges up to 20×20\times within seconds. The business requires an architecture that minimizes total cost of ownership by eliminating idle compute costs during off-peak hours while guaranteeing seamless responsiveness during sudden traffic spikes. Which TWO architectural decisions should you recommend to meet the business requirements? (Select TWO)

  1. Deploy the stateless web application on Cloud Run with minimum instances configured for baseline traffic and auto-scaling enabled for peak spikes.Answer
  2. Purchase Flexible Committed Use Discounts (CUDs) to cover the predictable minimum hourly compute spend across baseline workloads.Answer
  3. C
    Deploy the web platform on a multi-zone Google Kubernetes Engine (GKE) cluster with static node pools pre-provisioned for maximum peak flash sale capacity.
  4. D
    Purchase 3-year Resource-based Committed Use Discounts (CUDs) sized to match the maximum anticipated peak traffic volume.

Answer

The optimal architecture combines serverless deployment via Cloud Run (with minimum instances configured for baseline usage and auto-scaling for spikes) with Flexible Committed Use Discounts (CUDs) to lock in discounts for predictable baseline compute spend.
To satisfy both cost optimization and high availability during sudden 20×20\times traffic spikes, stateless HTTP workloads should leverage serverless container platforms like Cloud Run that automatically handle rapid scaling without requiring management of underlying node infrastructure. Combining this auto-scaling compute model with Flexible Committed Use Discounts (CUDs) targeted strictly at baseline consumption ensures maximum cost reduction for steady usage without over-committing capital on burst capacity.

Step-by-Step Solution

1
Analyze workload traffic profile and cost constraints
Identified a low, steady baseline combined with extreme, bursty 20×20\times traffic spikes during flash sales.
Cost optimization requires paying only for baseline infrastructure continuously while dynamically scaling compute resources during spikes.
2
Select serverless compute platform suitable for bursty stateless HTTP workloads
Selected Cloud Run with minimum instances for baseline readiness and autoscaling for flash sales.
Cloud Run scales rapidly to handle incoming HTTP requests and scales down when traffic subsides, preventing idle server charges.
3
Apply financial optimization mechanisms for baseline usage
Applied Flexible Committed Use Discounts (CUDs) tuned to baseline hourly usage.
CUDs lower hourly compute rates for predictable minimum commitments without financial exposure to unpredictable peak bursts.

Key Concept

Combining serverless auto-scaling compute platforms with baseline Committed Use Discounts (CUDs) to optimize cloud cost against highly bursty business workloads.
Estimated Time:2m 0s
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Topics

Designing Infrastructure for Business Requirements and Cost Optimization