Kubernetes has played a crucial role in comprehensively revolutionizing the manner in which we deploy and manage applications in the cloud. However, it comes with its share of complexity and challenges, especially when it comes to capacity planning.
Capacity planning involves identifying, assessing, and finalizing the total amount of resources you require for particular workloads or applications. Capacity planning for Kubernetes can get tricky if you are unaware of the manner in which CPU and memory resources get allocated for Kubernetes components and systems. The required resources are determined by the node type as well as a range of Kubernetes components.
Along with the applications, every Kubernetes self-managed cluster node and managed service contains a range of components. When you are capacity planning, it is crucial to keep in mind the Memory and CPU usage of the operating system, Kubernetes system services, as well as your applications. This helps you ensure that no CPU and memory are wasted.
Kubernetes clusters need proper resource planning in particular, as they carry out hard checks, ending and transferring workloads on the basis of resource usage. Its current infrastructure may convince a user that it can scale up and down without any consequence. However, there is immense energy required in this process.
Let us examine some of the factors to consider while carrying out Kubernetes capacity planning.
Your container resource request needs to be determined by the expected needs of the container to operate optimally in most expected scenarios. These expectations are not entirely unknown to most users. While they undergo some degree of change, they are in no manner a complete unknown. Also, remember to be a little generous with your calculations to account for any unexpected changes.
A helpful indicator of whether you have sized your containers correctly is how often it uses its scaling mechanism. The limits you have set should be to account for unexpected activity surges. They also prohibit performance defects from spreading further, cutting out any further risks for the clusters.
The next aspect to keep in mind is node size and volume. Here again, you must plan for expected usage, not best-case scenarios. Make it a point to calculate appropriate requests for all your workloads and set them accordingly. Let the environments shape the size and amount of nodes allocated. However, keep in mind that overcomplicating clusters can lead to increased usage.
Along with node capacity, it also helps to keep an eye on pod capacity. Pods are the smallest deployable units offered by Kubernetes, representing one or more containers that share the same resources, like CPU and memory. Keep the capabilities of your pods in mind while planning capacity. Identifying the number of pods required to handle expected traffic can help you control unnecessary resource utilization and balance usage.
With a better understanding of the resources at hand, our next task is to deal with any resource imbalance in the system. Each application has its inclinations – while some may be CPU-intensive, others are memory-intensive. Kube-scheduler works to schedule every workload to the most appropriate node, depending on the resource constraints. While you can tune the scheduler’s performance based on certain factors, it is important to choose the correct node type to reduce instances of unnecessary scaling and non-usage of resources.
One option for dealing with such imbalance is creating distinct node pools for various application types. These can then be controlled through node taints and affinity rules.
Setting Resource Limits & Requests
Kubernetes offers the useful ability to set particular resource requests and limits on the workloads you choose. While requests define the lower bound on the resource usage for each workload, limits establish the upper bound on the same.
Kubernetes uses the request parameter per workload to allocate the necessary CPU and memory. You can set specific resource requests and limits on the workloads of your choosing. Setting requests and limits that serve your requirements, you can optimize your infrastructure utilization while safeguarding application performance.
Setting resource limits strategically is essential for the success of your Kubernetes cluster. As mentioned earlier, low memory limits can lead to Kubernetes killing the application for violating set limits. Similarly, limits that are set unnecessarily high will lead to you ending up with a higher bill.
In a perfect scenario, your resources would be used at full capacity without ever crossing their limits. However, usage can often be irregular and allowing for fluctuations is necessary to safeguard your application.
A general tip to keep in mind would be to benchmark your usual usage and provide a 25% margin on your limits. It is also recommended to run load tests to record degradation and failures in performance.
Monitoring And Setting Up Alerts
Periodic monitoring and establishing alert systems are essential for capacity planning in Kubernetes. Once you have adjusted your nodes, make it a point to check that you are not overloading them by tracking their performance. There are several industry tools that you can use to provide performance alerts as and when needed.
Your goal for capacity planning of your Kubernetes cluster is to ensure your applications run efficiently and cost-effectively. To this end, capacity planning will be an ongoing process that requires periodic assessments and adjustments. With proper capacity planning, you can ensure that your applications are well-equipped to handle expected loads and scales in the future.