Cloud cost optimization is finding ways to run applications in the cloud, performing work or providing value to the business, at the lowest possible cost, and using cloud providers as cost-efficiently as possible. Optimization as a practice ranges from simple business management to complex scientific and engineering areas like operations research, decision science and analytics, and modeling and forecasting.
In a DevOps environment, cloud optimization refers to determining the most efficient way to allocate cloud resources among different use cases. The goal is usually to reduce waste while increasing performance in the cloud.
Different companies may have a different cloud optimization definition, depending on their cloud applications. A good cloud optimization strategy can help you see what you are doing right and where you need to improve to make the most out of your move to the cloud and IT spend.
Cloud optimization is essential for companies that want to see more cloud benefits such as reducing cloud costs, boosting their engineers’ productivity, and moving more operations from on-premises architecture to a cloud environment. Using a cloud service provider can offer many benefits, like the ability to easily scale up and down based on need, but unless you’re using the right services and tools, your costs can quickly spiral out of control.
Strategies
1. Find Unused or Unattached Resources
The easiest way to optimize cloud costs is to look for unused or unattached resources. Often an administrator or developer might “spin up” a temporary server to perform a function, and forget to turn it off when the job finishes. In another common use case, the administrator may forget to remove storage attached to instances they terminate. This happens frequently in IT departments across the company.
2. Identify and Consolidate Idle Resources
The next step in optimizing cloud computing costs is to address idle resources. An idle computing instance might have a CPU utilization level of 1-5%. When an enterprise receives a bill for 100% of that computing instance, it is a significant waste. A key cloud cost optimization strategy would be to identify such instances and consolidate computing jobs onto few.
3. Utilize Heat Maps
Heat maps are important mechanisms for cloud cost optimization. A heat map is a visual tool showing peaks and valleys in computing demand. This information can be valuable in establishing start and stop times to reduce costs. For example, heat maps can indicate whether development servers can safely shut down on weekends.
While administrators can shut down servers manually, a better option is to leverage automation to schedule instances to start and stop, thereby optimizing costs.
4.Rightsizing the computer resources
Modifying the computer services and resources to their most efficient size is called rightsizing. In some cases, developers select the incorrect instance size or unanimously spin resources in the cloud, making them run idle in the cloud. Such resources take up the unwanted space and make the cloud cost goes high.
5.Minimizing the API cost
Organizations migrate large workloads and APIs to the cloud during the migration. The accumulation of many files raises the cloud cost. Generally, on a cloud, the APIs get charged per object, irrespective of the size. You pay the same amount for the 1 GB file and 1 MB file. Hence, using the APIs in batch size integrates different APIs in batches and reduces the surplus cost.
Choose a pricing model
Cloud vendors usually offer two charging models. In the allocation-based model, providers charge for the provided services regardless of usage. Alternatively, providers can follow the consumption-based model to bill companies based on the resources they’ve utilized.
Most organizations will benefit from the consumption-based model. However, you should design your architecture based on the expected usage to minimize spendings.
Conclusion
Cloud Cost efficiency should be included in the considerations of design and engineering cloud applications, using the cloud-native philosophy. Unite and employ the perspectives and strengths of management, finance, analytics and engineering with the common goal of cost efficiency.