In context to FinOps, resource utilization is about ensuring there is sufficient business value for the cloud costs associated with each class or type of resource being consumed. It is necessary to observe a resource’s utilization over time to understand if the performance, availability or other quality metrics are of value for the expense incurred.
For compute resources, there may be times when it is deemed that for performance or availability gains, average utilization may need to decrease and the extra expense incurred is worth the value creation the resource provides. Or the opposite may be true and performance expectations can be lowered to improve cost. For these decisions to be made, resource utilization, efficiency and cost must be looked at together.
By comparison, for storage resources, it is necessary to estimate the latent inefficiency in the stored data, and by extension the potential gross savings that can be realized by removing, or rightsizing, that inefficiency. Keep in mind that different data sets have unique latent inefficiencies and require tailored approaches. For example, highly compressible (yet uncompressed) data has relatively high latent inefficiency, whereas encrypted data has relatively low (or no) latent inefficiency. And data that is infrequently accessed but stored in a high cost, high performance storage class (or tier) also has relatively high latent inefficiency. It is then necessary to estimate the cost of applying data efficiency infrastructure to thus realize net savings, along with the performance and availability impact of that infrastructure to ensure it meets the needs of users and applications.
The management of resource utilization and efficiency translates into identifying whether there is scope to reduce resource costs while maintaining the required performance and, if there is, making the changes required where it is economically worthwhile to do so.
Measures of success are represented in the context of cloud costs and may include one or more key performance indicators ( KPI ), describe objectives with key results ( OKR ), and declare thresholds defining outliers or acceptable variance from forecasted trends.
the information used that contributes to the measure(s) of success listed above; information here may include specific datasources, reports or any relevant input
Guide
This project helps practitioners understand how they can positively contribute to their organizations drive to reduce digital carbon footprint and accelerate their path to net-zero.
Guide
Data efficiency in the cloud involves the application of a wide range of technologies and architectural approaches to reduce the cost and time to store, access and transfer data. This guide extends the Framework Capability 'Resource Utilization & Efficiency' to focus on cloud data efficiency.
External Article
If you’re looking to better understand FinOps best practices for architecting your cloud workloads, you'll find the case study outlined helpful and practical.
User Story
Failure to purchase org level capacity commitments for BigQuery can result in runaway costs due to on-demand query costs.
User Story
In this post I’ll share why cloud can both save, and not save, depending on your stage of cost optimization maturity. For illustration purposes, the stages of maturity in this post are split into crawl, walk, and run.
User Story
I recently joined a team and one of our accounts was spending an insane amount on DynamoDB
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