How to scale an application in Kubernetes?

Due to advancements in the DevOps company, every organization has started adopting the microservices approach. Organizations leveraged Docker to standardize the setup of microservices, from coding to packaging to delivery. Docker simplified containerization for packaging and distributing code. But when their numbers grow from one to thousands, Kubernetes came to the rescue when it came to handling or managing these Docker containers.

Kubernetes has emerged as the industry standard for coordinating or maintaining containers and distributing applications. And scaling those applications means making sure that they can cope with additional users and their load during peak periods. How? By dividing the load and distributing it over different nodes. Let’s look at how you can scale your apps on Kubernetes using various strategies.

Scaling Applications on Kubernetes

When deploying apps in Kubernetes, there are many architectural considerations that you must consider. One of the most crucial parts is how and when to grow your application using Kubernetes. Scaling an application with Kubernetes calls for a different strategy than scaling other services. Kubernetes Autoscaling provides you with an option for dynamically scaling up or down the number of pods in an app based on resource utilization or other user-defined triggers.

1. Monitor the app to know when to scale it:

   As you monitor the application, you should keep two things in mind. First, keep track of how many individuals/users interact with the app. If a large number of people are accessing and engaging with your app, but it is responding slowly, you should scale to guarantee that users can still complete their tasks.

   Second, examine your nodes’ CPU as well as memory use. In general, you want to maintain memory consumption around 70% and CPU usage under 80%. If the application consumes substantially more than this, you may need to scale. There are several methods for monitoring your application.

2. Use cloud providers and resource limitations for streamlining the scaling process:

   The majority of cloud providers provide some form of autoscaling features. These systems employ several criteria to determine when to start and stop new instances, which are frequently the same ones you can monitor yourself. Many of these solutions will also leverage resource constraints to aid in the automation of your scaling process. Resource limitations are a method of imposing a maximum value on a measure. If a metric exceeds a certain threshold, the system will take action. For example, if your application requires a specific amount of RAM or CPU and those metrics exceed their bounds, autoscaling will assist in scaling the program.

   If the app takes too much of one resource, such as memory, but not enough of another, such as CPU, you may set a resource restriction for the first. And have the autoscaling feature monitor the second. If the second measure exceeds the limit, the autoscaling function might respond by spawning more instances of the app.

3. Maintaining a buffer of mission-critical apps:

   When you feel that a certain app or process is important for the company, you want to be on secure ground and have an extra buffer in the form of pod replicas along with additional resources. This is to ensure that it does not go down and disrupt business operations.

Autoscaling in Kubernetes

Autoscaling happens when you set up the app to automatically transform the number of pods according to current demand & resource availability. For instance, if there are too few pods operating, the system may automatically produce new pods to fulfill demand. However, if there are too many pods, you may scale down. In Kubernetes, there are three forms of autoscaling: Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler. Let’s take a closer look at each option.

1. Horizontal Pod Autoscaler

   HPA (Horizontal Pod Autoscaler) allows you to perform the automated scaling of the number of pods in a certain replication controller or deployment. A replication controller is a logical set of pods that have been generated and are viewed as a single entity. A deployment, on the other hand, is a logical set of pods generated as one or more pods. HPA is set up as a Kubernetes resource. And its state is controlled separately. The HPA refreshes the state to reflect the current situation as new pods are produced.

2. Vertical Pod Autoscaler

   VPA (Vertical Pod Autoscaler) enables automated scaling of a certain pod type’s CPU and memory resource limitations (depending on the name in the type field). The VPA resource has two fields: scale (or autoscale) and target (or target utilization). The maximum number of pods of that kind permitted in the cluster is determined by the scale. The proportion of resource utilization relative to the limit is determined by the target utilization. For example, suppose you have one sort of pod that consumes 10% of the CPU. A VPA with a goal utilization of 10% will scale that type of pod up to the maximum quantity permitted by the VPA (100%).

3. Cluster Autoscaler

   Based on observed resource utilization and user-defined parameters, the cluster autoscaler adjusts the number of pods in a cluster. It is intended to increase or decrease the number of pods based on parameters such as CPU utilization in a certain namespace. Cluster Autoscaler is set up as a Kubernetes resource with a scale (the maximum number of pods) and metrics (custom metrics). The autoscaler adjusts the state to reflect the current situation as additional pods are generated or destroyed.

Manual Scaling in Kubernetes

There are several manual scaling controls available in Kubernetes for cluster resources. These include – The command “kubectl scale”

Administrators can modify the size of a job, deployment, or replication controller quickly by using the kubectl scale command.

Declarative Scalability with Kubernetes

To handle Kubernetes scaling needs, pod autoscalers may be declaratively created using Kubernetes. By defining a policy in the behavior part of the autoscaler’s specs, you may control the greatest rate of change while scaling up or down.

The duration of the policy’s validity is specified by the periodSeconds specification. At most four copies may be scaled down every minute using the top policy value. According to the lower policy setting, just 10% of the current replicas can be shrunk in a minute.

Conclusion

Understanding how to scale an application with Kubernetes is necessary. To accommodate all the requests, an application would need to scale, for instance, if it has high traffic. However, make sure you know the available techniques that are ideal for your application before you grow your application. Since Kubernetes Autoscaling has received the most attention, I hope this post has given you a better understanding of the many approaches you may take to scale your application using Kubernetes.

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