A financial analytics company is designing the compute architecture on Google Compute Engine for two distinct backend services:
1. Batch Risk Simulation Engine: A highly fault-tolerant, stateless workload that processes data chunks independently and can handle sudden VM preemption without losing progress.
2. In-Memory Ledger Cache: A dedicated 24/7 database service that requires a custom ratio of 6 vCPUs and 45 GB of RAM to avoid paying for unused vCPUs.
Which TWO architectural decisions should the cloud engineer select to optimize both performance and cost for these workloads?
- Provision Spot VMs (or Preemptible VMs) for the Batch Risk Simulation Engine to achieve significant cost savings.Cevap
- Configure a Custom Machine Type with extended memory for the In-Memory Ledger Cache to match the exact vCPU and RAM requirements.Cevap
- CProvision Spot VMs for the In-Memory Ledger Cache to minimize baseline hourly costs.
- DDeploy the Batch Risk Simulation Engine on Cloud Functions to automatically manage scaling without using virtual machines.
Cevap
The correct decisions are to use Spot VMs for the fault-tolerant batch risk simulation engine and to configure a Custom Machine Type with extended memory for the 24/7 in-memory cache.
Selecting Spot VMs for the batch simulation engine takes advantage of discount pricing suitable for fault-tolerant workloads. Configuring a Custom Machine Type for the cache allows exact allocation of 6 vCPUs and 45 GB RAM without paying for unused capacity in standard predefined shapes.
Adım Adım Çözüm
Anahtar Kavram
Compute Engine Machine Type Selection and Spot VM Suitability
Tahmini Süre:1m 30s