An enterprise is reviewing its monthly Google Cloud infrastructure spend to optimize Operational Expenditure (OpEx). An architectural audit reveals the following workload profiles: Production Web & Database Tier: Runs continuously (24/7) with steady, predictable CPU and memory demand across multiple regions. Batch Image Processing: Fault-tolerant, stateless processing jobs that can tolerate unexpected interruptions and node terminations. Legacy Application VMs: Individual virtual machines that have shown persistent CPU utilization below 10% and low memory consumption over the past several weeks. Which combination of optimization strategies should you implement to maximize cost savings while maintaining production reliability?

Apply machine type rightsizing recommendations to downsize underutilized legacy VMs, purchase Committed Use Discounts (CUDs) for baseline 24/7 production workloads, and provision Spot VMs for fault-tolerant batch processing.: ### What This Strategy Is This multi-tiered cost optimization strategy leverages **Active Assist rightsizing recommendations**, **Committed Use Discounts (CUDs)**, and **Spot Virtual Machines (Spot VMs)** to target each workload profile according to its operational requirements and fault tolerance. ### How It Meets The Requirements - **Legacy Application VMs**: Compute Engine generates **machine type recommendations** by analyzing historical CPU and memory utilization gathered by Cloud Monitoring. Resizing overprovisioned VMs eliminates wasted compute capacity without impacting performance. - **Production Tier**: Purchasing **Committed Use Discounts (CUDs)** (for 1-year or 3-year terms) provides significant price reductions (up to 57% or more for general-purpose compute) in exchange for a commitment to continuous baseline resource or spend usage. - **Batch Processing**: Deploying **Spot VMs** provides up to 60-91% discounts compared to standard on-demand pricing. Because the batch rendering pipeline is stateless and fault-tolerant, interruptions caused by Compute Engine reclaiming capacity do not risk data integrity. ### Key Advantages - **Maximizes OpEx efficiency**: Combines discounts on commitment, spare capacity, and right-sized infrastructure for maximum total cost of ownership (TCO) reduction. - **Preserves high availability**: Ensures critical 24/7 production services remain on stable, non-preemptible resources backed by contract discounts. - **Automates optimization insights**: Utilizes Google Cloud's built-in metric analysis to continuously identify overprovisioned instances. ### Why This Is Optimal Aligning discounting mechanisms to workload characteristics ensures that you do not overpay for guaranteed availability where it is unnecessary (the batch tier), while securing the highest predictable savings on continuous baseline architectures.

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Compute Resource Cost Optimization Cuds Spot Rightsizing

Section: Cost optimization/resource optimization (CapEx/OpEx)

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An enterprise is reviewing its monthly Google Cloud infrastructure spend to optimize Operational Expenditure (OpEx). An architectural audit reveals the following workload profiles:

Production Web & Database Tier: Runs continuously (24/7) with steady, predictable CPU and memory demand across multiple regions.
Batch Image Processing: Fault-tolerant, stateless processing jobs that can tolerate unexpected interruptions and node terminations.
Legacy Application VMs: Individual virtual machines that have shown persistent CPU utilization below 10% and low memory consumption over the past several weeks.

Which combination of optimization strategies should you implement to maximize cost savings while maintaining production reliability?

Consolidate all three workloads into a single Managed Instance Group (MIG) using preemptible allocation quotas with standard CUDs, while disabling machine rightsizing.

Apply machine type rightsizing recommendations to downsize underutilized legacy VMs, purchase Committed Use Discounts (CUDs) for baseline 24/7 production workloads, and provision Spot VMs for fault-tolerant batch processing.

Migrate the 24/7 production workloads to Spot VMs to achieve maximum discounts, purchase 3-year CUDs for batch processing, and rely on Sustained Use Discounts (SUDs) for the legacy application VMs.

Apply rightsizing recommendations to the legacy VMs, provision uncommitted On-Demand Zonal Reservations for production workloads, and configure Sole-Tenant Nodes with CPU overcommit for batch jobs.

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Compute Resource Cost Optimization Cuds Spot Rightsizing

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