GKE

• Building a Culture:

  • Organizations should focus on educating technical teams about Kubernetes best practices to optimize costs effectively.
  • Transparency and awareness among developers and operators regarding application costs are essential.
  • Implementing guardrails and policies can prevent unexpected cost escalations and enforce resource quotas.
• Best Practices - Technical Side:

  • Review small development clusters:
  • Consider using multi-tenant clusters for development to save on overhead.
  • Utilize namespaces and policies to cap and isolate resources for experimentation.
  • Disable or limit unnecessary add-ons like Cloud Logging, Monitoring, Horizontal Pod Autoscaling, Kubernetes Dashboard, and Kube DNS to reduce costs.
• Add Pod Disruption Budgets:

  • Set thresholds for the number or percentage of pods that can be disrupted during voluntary actions like upgrades or autoscaling.
  • Determine the minimum number of pods needed to run without disrupting application users, considering each application independently.
  • Pod disruption budgets help maintain application availability during autoscaling without overprovisioning.
• GKE Architecture:

  
• • Review small development clusters:
  • Consider using multi-tenant clusters for development to save on overhead.

  
• PodDisruptionBudgets:

  • Determine the minimum number of pods needed to run without disrupting application users, considering each application independently.
  • Pod disruption budgets help maintain application availability during autoscaling without overprovisioning.

  
• Observing GKE Clusters:

  • Organizations often have separate teams managing clusters and developing applications, necessitating collaboration to optimize costs effectively.
  • The monitoring dashboard provides crucial insights into resource utilization and performance metrics.
  • Cloud Operations, including Monitoring, Logging, and Alerting, offers detailed views into cluster metrics, aiding in identifying issues and optimizing resource usage.
• Observing GKE Clusters:

  • Utilization of the Infrastructure, Services, and Workload tabs provides different perspectives for analyzing cluster behavior.
  • Custom charts created using the Metrics Explorer offer deeper insights into specific metrics such as CPU usage time by container.
• Logging and Monitoring:

  • Cloud Logging and Cloud Monitoring offer observability into applications and infrastructure, with costs incurred based on usage and log retention duration.
  • Increased logging and custom metrics lead to higher costs, necessitating careful management.
  • Implementing multi-tenant logging with exclusion rules helps filter out irrelevant logs to minimize costs.
  • Further details and tips on cost optimization for logs and monitoring are available for reference.
• Enable GKE Usage Monitoring:

  • Enabling GKE Usage Monitoring allows automatic collection of granular metrics, exported to BigQuery for detailed analysis.
  • Insights into CPU, memory, storage, and network usage facilitate comparison with allocated resources, identifying over-allocated resources.
  • Namespace-based resource usage monitoring and utilization of labels for filtering enhance visibility into resource consumption and cost attribution.
• Kubernetes Resource Quotas:

  • Kubernetes Resource Quotas enable capping resource usage at the namespace level, ideal for multi-tenant clusters.
  • Limit ranges enforce resource limitations at the pod and cluster level, complementing Resource Quotas.
  • Detailed metrics inform the setting of appropriate quotas, preventing resource over-consumption and potential cost spikes.
• Metrics Server:

  • The Metrics Server collects and exposes metrics to the Kubernetes metrics API, aiding in autoscaling decisions.
  • Maintaining the metric server's stability is crucial to prevent disruptions in autoscaling operations, necessitating careful monitoring and version compatibility checks.
• CI/CD for Cost Optimization:

  • Reviewing configuration changes before deployment helps prevent unintended cost escalations, with tools like Anthos Policy Controller automating policy enforcement.
  • Integration of policy validation into CI/CD pipelines using tools like KPT ensures continuous validation of configurations throughout the development cycle.
• Recommendation Hub:

  • The Recommendation Hub provides actionable suggestions for optimizing costs based on usage patterns, spanning various aspects such as security and cost.
  • Recommendations tailored to specific usage patterns offer insights into potential cost-saving opportunities, such as optimizing compute costs through long-term commitments.
• GKE Monitoring:

  
• Resource and Limit Quotas:

  
• Metrics Servers: