An online gaming company is planning its Google Cloud Compute Engine architecture for two distinct operational workloads: a continuous, low-latency in-memory leaderboard service that runs 24/7 and cannot tolerate sudden terminations, and an overnight batch analytics pipeline that parses telemetry log files, runs for 6 hours, is stateless, and can resume from checkpoints if interrupted. You need to design a compute provisioning strategy that optimizes cost while satisfying the reliability requirements for both workloads. Which TWO compute configuration strategies should you implement?
- Provision the continuous, low-latency leaderboard service using standard Compute Engine instances backed by Committed Use Discounts (CUDs).Answer
- Provision the overnight batch analytics pipeline using Spot VMs in a Managed Instance Group (MIG).Answer
- CProvision the continuous, low-latency leaderboard service using Spot VMs to maximize hourly baseline cost savings.
- DCombine Sustained Use Discounts (SUDs) with Spot VM instances for the batch processing pipeline to accumulate additional automated pricing reductions.
Answer
The optimal strategy requires provisioning the 24/7 baseline leaderboard service using standard Compute Engine virtual machines backed by Committed Use Discounts (CUDs), while provisioning the fault-tolerant overnight batch processing pipeline on Spot VMs within a Managed Instance Group (MIG).
For continuous baseline workloads running 24/7, Committed Use Discounts (CUDs) provide guaranteed resource reservation and substantial cost savings without risking preemption. For stateless, fault-tolerant batch jobs that can withstand sudden interruptions, Spot VMs offer the maximum possible pricing discount on Compute Engine.
Step-by-Step Solution
Key Concept
Compute Engine Machine Type Selection and Cost Optimization Strategies