Describe a situation where a cost optimization strategy involving committed use discounts would be most beneficial, and illustrate how to apply these discounts effectively across different compute resources.
Committed Use Discounts (CUDs) are most beneficial for organizations with predictable and consistent compute resource needs over a sustained period. They provide significant cost savings compared to on-demand pricing, and are especially effective when used strategically across different types of Google Cloud compute resources.
Situation: A large SaaS company operates a web application that provides critical services to thousands of customers. This application requires a constant level of compute resources to maintain consistent performance and availability, 24/7, 365 days a year. The company utilizes various Google Cloud resources, including Compute Engine instances, Google Kubernetes Engine (GKE) nodes, and Cloud SQL databases. The traffic to the application is reasonably consistent day-to-day, and seasonal variations are predictable.
Why CUDs are Ideal Here:
Predictable Workloads: The SaaS company's application runs continuously with consistent traffic patterns. This predictability makes it a prime candidate for CUDs, as the company can forecast its resource usage with reasonable accuracy.
Cost Savings Potential: CUDs offer substantial discounts (up to 70% in some cases) compared to on-demand pricing. These discounts can result in considerable cost savings when applied across various resources.
Long-Term Commitment: The company is planning to operate the application for the foreseeable future and therefore is willing to commit to a certain level of resource usage. CUDs fit this long term commitment.
Resource Utilization: With predictable workloads the company will be able to utilize compute resources in a more efficient manner. Committing to specific resources will result in more effective utilization, as the company can forecast the needs.
How to Apply CUDs Effectively Across Different Compute Resources:
1. Compute Engine Instances:
Analysis: Review the historical usage of Compute Engine instances. Identify the instance types and number of virtual CPUs (vCPUs) used consistently for each environment (development, staging, production).
CUD Application: Purchase a CUD based on your consistent baseline usage. For example, if the production environment consistently runs 10 n1-standard-4 instances, purchase a CUD for 10 vCPUs on that instance type for a 1 year or 3 year term. The CUD will be applied to all instances matching the selected instance type.
Flexibility: Use committed use discounts to get the best price by committing to the right types of virtual machines. If there are different types of virtual machines for different purposes, then commit to each type separately to optimize cost.
Example: If the company consistently uses 20 n2-standard-8 instances for its production workload, they could commit to 20 vCPUs for the specified instance type. This would greatly reduce the cost of these instances as compared to the on-demand rate.
2. Google Kubernetes Engine (GKE) Nodes:
Analysis: Analyze the CPU, memory and GPU usage of GKE nodes across all node pools. Determine the minimum number of nodes necessary to run the application consistently. Identify the machine types of the nodes in each node pool.
CUD Application: Purchase a CUD for the consistent node types in GKE. The discount applies to all GKE nodes that match the commitment made. If there are multiple node pools with different instance types, purchase separate CUDs for each instance type in each pool based on the long-term sustained usage.
Auto-Scaling: While CUDs are best used on consistently used resources, use auto-scaling to handle bursts, but ensure your baseline is well covered by your CUDs. Auto-scaling will provide extra capacity in times of need, and can be controlled by setting a min-max range for the nodes.
Example: If a node pool consistently uses 50 n2-standard-4 instances, they can commit to 50 vCPUs, so any nodes that match this type will benefit from the committed use discounts. If there are separate node pools for processing with different instance types, then different commits can be made for these.
3. Cloud SQL Databases:
Analysis: Analyze the number of vCPUs and memory used by Cloud SQL databases. The goal is to identify the consistent resource usage to determine the optimal CUD.
CUD Application: Purchase a CUD based on the vCPUs and memory that a specific database instance consistently uses. CUDs apply at a regional level, so be sure to commit to the same instance type and resource configuration within each region.
Instance Sizing: Right-size the Cloud SQL instance to make sure you are not committing to more resources than necessary. Match the SQL instance to the appropriate resource needed for your application.
Example: If a database instance consistently uses 8 vCPUs and 32GB of memory, a CUD for these resources would significantly reduce the operational cost, provided the application can predict the long-term usage for these resource configurations.
4. Monitoring and Adjustments:
Regular Monitoring: Continuously monitor resource utilization with Cloud Monitoring. This is crucial to make sure that the commitment is effective in the long run and to confirm the application usage patterns.
Performance Reports: Generate usage reports to identify underutilized or overutilized resources. Use these reports to adjust the CUD commitments to optimize cost and performance.
Flexibility: You can make adjustments to your discounts. Purchase additional CUDs if usage increases, or modify CUDs as application requirements change.
5. Key Considerations:
Commitment Period: CUDs are available with 1-year and 3-year commitment periods. Longer commitment periods will provide more significant cost savings. Choose the period based on the organization’s future plans.
Resource Flexibility: You can change instance types or move to different Google Cloud regions, but within the scope of your purchased CUDs.
Planning: Plan carefully based on a thorough analysis of resource usage patterns. Improper planning may lead to wastage of resources and missed savings.
Organization Hierarchy: Make sure to properly configure the organization hierarchy and billing accounts, to get the best CUDs.
In summary, CUDs are an extremely powerful tool for optimizing costs, but must be strategically implemented. For the SaaS company with predictable and consistent resource usage patterns, the application of CUDs across Compute Engine, GKE, and Cloud SQL can result in substantial cost savings, while maintaining a high-performing and resilient application. Regularly monitor resource usage and make adjustments based on the organization's business needs and consumption patterns.