It’s crucial to manage resource availability and capacity to ensure the proper functioning of a software application. This process is called capacity management. Capacity management should cover the entire software development lifecycle from development to production. In this post, we will have a look at how we can implement capacity management practices at the infrastructure level.
What is Capacity Management?
Capacity management is the process of ensuring the availability of IT resources to handle the resources requirements throughout an application lifecycle. Capacity management is not only concerned about infrastructure resources, but also about manpower that is required throughout the software development lifecycle. However, at an infrastructure level, the main focus of capacity management is to ensure adequate infrastructure resources such as servers are available for nominal operations of the supported applications and services.
What is the Importance of Capacity Management at an Infrastructure Level?
Assume you have a containerized application running on a Kubernetes cluster, and you have configured your containers with Kubernetes CPU limits. However, your nodes do not have enough capacity to fulfill resource requirements, which will impact the application performance. On the other side, if the nodes are overprovisioned, you will be paying for resources not utilized by your application. With proper capacity management, both underprovisioning and overprovisioning can be avoided.
As I already said, capacity management covers the complete software development process. That means capacity management includes managing resources needed to develop, test, and maintain the application. For example, developers and QA will need test environments to develop and test the application. Provisioning these environments without proper planning can easily lead to unused resources that impact the bottom line of the development process.
With proper capacity management, users can reduce the operation’s expenditure without impacting the resource availability throughout the software development cycle. Additionally, this will also enable users to easily identify resource utilization and bottlenecks and quickly perform any optimizations or modifications without affecting the end-users.
How to Implement Capacity Management
Infrastructure capacity management can be implemented as a continuous three-step process, from planning the capacity requirements to monitoring the resource usage. Adapting this process will help you to easily implement capacity management in your infrastructure workflow.
Capacity Planning
In the first step of the process, users need to identify the capacity requirements across the software development process and create a plan to facilitate the requirements. In this stage, users will look at resource utilization and identify the requirements to meet the current and future workload demands. As there will be different types of resources, there will be different requirements for each type of resource. This will also help identify resource bottlenecks and overprovisioned resources.
Then, using the above data points, you have to develop a capacity management plan that would detail the resources and capacity provisioned across your environments.
Infrastructure Implementation
Based on the capacity management plan developed during capacity planning, you have to implement the changes to the infrastructure resources. This will include the following tasks.
- Reducing the capacity of overprovisioned resources
- Increasing the resources for any identified bottlenecks
- Creating usage alerts, budgets to monitor the resource usage
- Implementing workflows to manage resource provisioning and modifications, which would also help to eliminate shadow IT practices.
- Configuring savings plans and purchasing reserved instances for continuously operating resources
All the above steps ensure that proper capacity is provisioned at all times and any variance is quickly detected.
Real-Time Resource Monitoring
After the implementation process, these resources need to be constantly monitored to ensure the resources are properly utilized. This continuous monitoring process will look at resource metrics, including usage statistics. This monitoring will be the basis for maintaining adequate capacity throughout the application lifecycle and facilitating new capacity requirements.
If you discover a resource being either underutilized or overutilized, you have to change the capacity management plan to meet the new utilization metrics and implement them in your infrastructure.
Predictive Analysis in Capacity Management
Another aspect to think about when capacity management is predictive analysis. With predictive analysis, where you utilize historical data with statistical modeling and machine learning, users will be able to accurately predict future workload needs.
Predictive analysis in capacity management lets users be better prepared for future infrastructure demands, especially in constantly changing environments such as modern cloud-native applications. This takes the real-time monitoring a step further as this can predict usage demands even before they impact the actual resources. Predictive analysis can also be used as a strategic tool as opposed to an operational tool because this data can be used to get an idea about the overall growth of their applications.
Conclusion
Capacity management at the infrastructure level is an ideal mechanism to control resource requirements while ensuring resource availability for optimal application performance and managing operational infrastructure expenditure. If you implement a capacity management plan properly, all provisioned infrastructure resources within their environments will be kept at optimal capacity levels.
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