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Mastering Horizontal Scaling in Kubernetes Clusters

Explore Kubernetes clusters for peak traffic management. Master horizontal scaling, Cluster Autoscaler, and HPA for seamless digital experiences.

 Prince is a technical writer and DevOps engineer who believes in the power of showing up. He is passionate about helping others learn and grow through writing and coding.
Prince Onyeanuna
March 4, 2024 | 12 min read
Master K8s Clustering

A growing number of organizations and companies are choosing modern solutions like Kubernetes clusters to ensure optimal performance during peak periods. For instance, on Black Friday, the busiest shopping day of the year, online retailers face a massive influx of traffic as shoppers flock to their websites searching for the best deals. These websites are bustling with activity as customers eagerly fill their carts with items.

In regular setups, such high demand could cause the app to slow down or even crash. Now, not every day is Black Friday, but many of these businesses are facing an ever-increasing demand from their customers to keep pace in a digital age. Many results of traditional setups involved predicting the necessary server capacity in advance and setting up additional servers accordingly. It was like guessing the number of chairs you'd need for a party and hoping you got it right.

But with Kubernetes, it's like having magical chairs that appear whenever more people show up to the party. Kubernetes can automatically add more servers, whether physical or virtual when needed to handle extra traffic, and it can remove them when things quiet down. So, it's like having a flexible party space that adjusts to the number of guests in real-time.


This article will explore the concept of horizontal scaling in Kubernetes Clusters. You will gain a good understanding of the two types of horizontal scaling in Kubernetes, including the Cluster Autoscaler and Horizontal Pod Autoscaler (HPA). At the end of this article, you will have a clear understanding of how scaling in Kubernetes works and be a Kubernetes Clusters pro!

Understanding Horizontal Scaling in Kubernetes Clusters

Horizontal scaling

On the other hand, vertical scaling, or "Scale-up," entails increasing the capacity of a single resource, such as adding more CPU or memory to a server. With vertical scaling, the server's capacity is augmented to handle a growing number of requests. In Kubernetes, this involves adjusting the CPU and memory allocation of pods.

Horizontal scaling in Kubernetes offers several advantages that enhance the efficiency and resilience of applications. Firstly, it enables dynamic scaling, allowing resources to be dynamically allocated or removed in response to fluctuating demands. This agility ensures that applications can cope with sudden increases in traffic without experiencing downtime or performance issues.

Moreover, horizontal scaling improves resilience by distributing workloads across multiple systems, or pods, in Kubernetes. This redundancy reduces the risk of single points of failure and enhances the overall stability of the system.

Additionally, Kubernetes automates the scaling process, eliminating the need for manual intervention. This automation streamlines operations, reduces the risk of errors, and allows teams to focus on higher-level tasks, thereby improving productivity and resource utilization.

Types of Horizontal Scaling in Kubernetes Clusters

Kubernetes offers several mechanisms for scaling both horizontally and vertically. However, this section will focus on the horizontal scaling options available in Kubernetes, including the Cluster Autoscaler and Horizontal Pod Autoscaler (HPA).

Cluster Autoscaler

The Cluster Autoscaler is a Kubernetes component. It automatically adjusts the cluster's size when there aren't enough resources for pods. It can make the cluster bigger or smaller based on pod resource requirements and cluster capacity.

You can set up the Cluster Autoscaler to work with a Kubernetes cluster on different cloud providers. It can be customized to consider things like node group sizes and instance types. This helps optimize the scaling process for your specific setup.

The Cluster Autoscaler keeps an eye on pod resource requests and the Kubernetes cluster's capacity. If a pod can't be scheduled due to low resources, it asks the cloud provider for more nodes. And if nodes aren't being fully used, it can remove them to save resources.

How Cluster Autoscaler Works

The Cluster Autoscaler becomes active under specific conditions. It triggers when either of the following scenarios occurs:

  1. Pods fail to run in the cluster due to insufficient resources.
  2. Nodes within the cluster remain underutilized for an extended period, allowing their pods to be placed on other existing nodes.

In a scale-out scenario, the Cluster Autoscaler constantly monitors the Kubernetes cluster for pending pods. When it detects pending pods, it initiates the addition of new nodes to scale out the cluster. Its integration with public cloud platforms allows the Cluster Autoscaler to provision additional virtual machines, which Kubernetes recognizes as new nodes.

Subsequently, the Kubernetes scheduler evenly distributes pending pods across these newly added nodes, optimizing resource utilization and workload distribution. This automated process ensures that the cluster dynamically scales out to accommodate increased demand, preserving performance and availability without requiring manual intervention.

To implement the Cluster Autoscaler in your Kubernetes cluster, follow the official documentation for your cloud provider on the Kubernetes autoscaler repository.

Horizontal Pod Autoscaler (HPA)

The HorizontalPodAutoscaler is a Kubernetes component that automatically scales the number of pods in a deployment, replication controller, or replica set. It adjusts the number of pods based on observed CPU utilization or other custom metrics.

When demand increases, HPA automatically increases the number of pod replicas to ensure that sufficient resources are available to handle incoming requests efficiently. Conversely, when demand decreases, HPA scales down the number of pod replicas to prevent resource wastage and optimize resource utilization. HPA achieves this by continuously monitoring specified metrics, such as CPU or memory utilization, and comparing them against predefined thresholds.

The HorizontalPodAutoscaler operates as both a Kubernetes API resource and a controller. The resource defines the behavior of the controller. This Kube controller manager, residing within the Kubernetes control plane, regularly adjusts the desired scale of its target (e.g., a Deployment) based on observed metrics such as average CPU utilization. This automated process guarantees that the workload dynamically adapts to changing demand.

How Horizontal Pod Autoscaler Works

HorizontalPodAutoscaler operates periodically, not continuously, with intervals typically set to 15 seconds by default. During each interval, the controller manager queries resource utilization against specified metrics in each HPA definition. It identifies the target resource, selects pods based on labels, and retrieves metrics from either the resource metrics API or custom metrics API.

For per-pod resource metrics like CPU, the controller fetches metrics for each pod. It calculates utilization percentages and takes the mean across all pods to determine a scaling ratio. If CPU utilization surpasses a predefined threshold, HPA automatically scales up pod replicas to handle increased load.

Conversely, during periods of low demand, HPA scales down pod replicas to conserve resources. This dynamic adjustment ensures efficient resource utilization and consistent performance, enhancing the scalability and cost-effectiveness of Kubernetes deployments.

To know more about Horizontal Pod Autoscaler, refer to the official Kubernetes documentation on Horizontal Pod Autoscaler.

Strategies for Effective Horizontal Scaling in Kubernetes Clusters

Effective horizontal scaling in Kubernetes can be achieved through various strategies:

  1. Setting Resource Requests and Limits: Accurate resource allocation is essential for efficient scaling. By defining resource requests and limits in pod specifications, Kubernetes can allocate the necessary CPU and memory resources to each pod. This ensures that pods have enough resources to handle their workload without wasting resources.
  2. Designing Stateful Applications for Scalability: Scaling stateful workloads comes with unique challenges, such as maintaining data consistency and preserving state across pod replicas. Implementing statefulsets is a recommended strategy, as they provide stable and unique network identifiers for each pod. This ensures reliable and consistent access to data across replicas.
  3. Utilizing Kubernetes Controllers: Kubernetes controllers such as ReplicationControllers, ReplicaSets, and Deployments play a crucial role in automated scaling and workload management. ReplicationControllers maintain a specified number of pod replicas, ensuring continuous availability. ReplicaSets offers advanced features like rolling updates and scaling based on labels. Deployments provide a higher-level abstraction for managing application deployments, simplifying scaling, updates, and rollbacks for complex applications.

Monitoring Kubernetes Clusters

With the right monitoring tools in place, you can keep a close eye on how your cluster is doing, spot any potential issues, and then take action to optimize your resource allocation.

By utilizing Kubernetes Cluster metrics such as CPU usage and custom monitoring solutions like Prometheus or Datadog, you can gain a clear understanding of your Kubernetes cluster's performance. You'll be able to see things like CPU and memory usage, pod and node status, and even network traffic. This data is important because it helps you understand exactly how your Kubernetes clusters are performing. You can see if you're using your resources efficiently or if any bottlenecks need to be addressed.

With these monitoring data at your fingertips, you can optimize your horizontal scaling strategies. By looking at trends over time, you can find areas where you can fine-tune your resource allocation. You may need to adjust your scaling thresholds or tweak your resource requests and limits. You can find ways to optimize how your pods are scheduled, which can improve your resource usage and save you money.

With these monitoring insights, you can also plan for the future. By spotting trends and anticipating your future resource needs, you can scale your Kubernetes Clusters proactively. This means you'll always be prepared to handle whatever comes your way, whether it's a sudden spike in traffic or steady growth over time.

Kubernetes Clusters are a Powerful Development Tool

From the discussion above, it's clear that horizontal scaling in Kubernetes Clusters is a powerful tool for managing varying workloads and ensuring the efficient use of resources. By leveraging both types of Kubernetes horizontal scaling mechanisms, you can ensure that your applications are always running at their best, no matter what the demand.

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