A bio-pharmaceutical research laboratory is planning a Google Kubernetes Engine (GKE) cluster architecture to execute fault-tolerant genomic sequencing batch workloads. The processing engine requires custom Linux kernel sysctl modifications directly on the underlying host node OS to optimize memory paging. Additionally, the finance team requires minimizing compute expenditure for these stateless batch processing jobs. Which TWO architectural decisions should the cloud team implement to satisfy all requirements?
- Provision the cluster using GKE Standard mode to allow custom node pool configurations and node OS kernel tuning.Answer
- Configure node pools using Spot VMs to run the fault-tolerant batch processing workloads at a significantly lower cost.Answer
- CDeploy the workload on GKE Autopilot mode so Google automatically applies the necessary custom kernel sysctl parameters to the worker nodes.
- DConfigure the Horizontal Pod Autoscaler (HPA) to provision additional Compute Engine worker VM instances when genomic processing queues increase.
- EAvoid Spot VMs and mandate standard On-Demand instances, as GKE node pools do not support fault-tolerant compute configurations.
Answer
The team should select GKE Standard mode to support custom host sysctl kernel parameters and configure node pools with Spot VMs for cost optimization on stateless batch jobs.
Choosing GKE Standard mode grants administrative control over node OS configuration, allowing custom sysctl kernel adjustments needed for memory paging. Combining this with Spot VM node pools optimizes compute expenditure for stateless, fault-tolerant batch workloads.
Step-by-Step Solution
Key Concept
GKE Cluster Modes & Node Pool Compute Types