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Jan 11, 2024 | 6 Minute Read

How To Monitor And Scale Your Kubernetes Workload

Hanush Kumar, Marketing Associate

Table of Contents


Adopting the cloud-native approach can be expensive when you manually scale up and down your resources. Users may also face frequent service failures due to a lack of resources for handling the load.

Monitoring Kubernetes workloads and utilizing an autoscaling option can help solve these challenges.

What Is Kubernetes Monitoring?

Kubernetes can be monitored by keeping tabs on several metrics. These metrics play a crucial role in configuring dashboard activities and alerts, providing valuable insights into both the Kubernetes system and the applications operating within it.

The Kubernetes monitoring metrics can be sourced from various providers, such as cAdvisor, Metrics Server, Kubernetes API Server, and Kube-state-metrics.

Types Of Kubernetes Monitoring

There are also different types of Kubernetes monitoring.

  1. Cluster Monitoring

    The Kubernetes cluster functions as the central host for all containers and the machinery executing applications. For effective container management, it’s important to oversee the environment and the health of all cluster components, like:

    • Cluster Nodes

    Within a cluster, nodes facilitate the execution of applications through several resources. It is crucial to monitor and observe the health of these resources. Worker nodes are responsible for hosting containers, while master nodes oversee the activities of the worker nodes.  

    • Cluster Pods

    A pod, the smallest unit within a cluster, is composed of one or more containers. The quantity of active pods directly influences the number of nodes required. Monitoring the health and resource utilization of pods is essential for effective Kubernetes oversight.

    • Resource Utilization

    Gaining insights into resource utilization metrics helps understand the capabilities and limitations of cluster nodes, aiding in the assessment of sufficiency and redundancy. Some essential resources to track are disk utilization, memory utilization, CPU utilization, and network bandwidth.

  2. Pod Monitoring

    Pods, composed of containers deployed on nodes, form a fundamental component of the Kubernetes ecosystem. It is necessary to monitor pods by evaluating the following metrics.

    1. Container Metrics

      Comprehend and manage the number of containers within a pod, along with their lifecycle. Strive to prevent pod overload and optimize for scalability.
    2. Application Metrics

      Performance metrics for applications gauge performance levels and furnish industry-specific data. These metrics provide valuable insights into traffic, the frequency of unsuccessful requests, request durations, and feature utilization.
    3. Kubernetes Scaling And Availability Metrics

      Comprehending Kubernetes' scaling and availability is important for configuring auto-scaling tools within clusters. The node requirements are influenced by the number of containers or pods present in a cluster.
    4. Load Average

      Load average reflects the count of programs in execution or waiting to be executed on the CPU. It's essential to keep it within the limit of the number of CPU cores. For effective troubleshooting, monitor load average in conjunction with sys CPU usage and I/O wait.
    5. Resource Requests And Limits

      Containers come with designated resource requests and limits for CPU and memory. It's crucial to efficiently handle these to prevent either underutilization or overutilization. Strive for a target of approximately 80% actual usage on the 90th percentile for both resource requests and limits.

Types Of Autoscaling Options In Kubernetes

In Kubernetes, a cluster is a group of machines that execute containerized applications. At its core, a cluster contains a set of nodes and a control plane. The control plane is responsible for preserving the desired state of the cluster, including the specific applications running and the associated images.

On the other hand, nodes represent the virtual or physical machines responsible for executing applications and workloads, also known as pods. These pods are composed of containers that request computational resources like CPU, Memory, or GPU.

Kubernetes Master and Worker Nodes

Pod-Based Scaling

Pod-based scaling is the ability of applications or services in a containerized environment to scale by modifying the number of pods. In Kubernetes, a pod is the smallest deployable unit that houses one or more containers running together on a node. Containers within a pod share the same network namespace and can communicate with each other using localhost.

  • Horizontal Pod Autoscaling

Horizontal scaling involves adjusting the computational resources within an existing cluster, such as adding new nodes or increasing pod counts. This can be done by using the Horizontal Pod Autoscaler (HPA) to raise the replica count.

Horizontal Pod Autoscaler

The Horizontal Pod Autoscaler (HPA) is a tool designed to dynamically adjust the number of pods in a cluster based on the current computational workload demands of an application. It assesses the required number of pods using user-defined metrics, typically CPU and RAM usage, but it also supports custom metrics.

The HPA continuously monitors CPU and memory metrics provided by the installed metrics server in the Kubernetes cluster. When a specified threshold is reached, the HPA initiates the creation or deletion of pods to maintain the desired number based on the set metrics. This involves updating the number of pod replicas within the deployment controller.

Consequently, the deployment controller scales the number of pods up or down until it aligns with the desired count. If custom metrics are preferred to dictate scaling rules for pods through the HPA, the cluster needs to be connected to a time-series database to store the relevant metrics. Horizontal Pod Autoscaling cannot be applied to objects that cannot be scaled, such as DaemonSets.

  • Vertical Pod Autoscaling

Vertical scaling involves adjusting the inherent resources, such as CPU or RAM, for each node within the cluster. Typically, this entails creating a new node pool using machines with varying hardware configurations.

Vertical Pod Autoscaler

In the context of pods, vertical scaling dynamically tunes the resource requests and limits based on the present application's needs. This process is facilitated by the Vertical Pod Autoscaler (VPA).

The Vertical Pod Autoscaler (VPA) allocated necessary CPU and memory resources to existing pods, modifying the available computational resources for an application. This functionality ensures effective monitoring and adaptation of the allocated resources of each pod throughout its lifecycle.

Accompanied by a tool named VPA Recommender, the VPA assesses current and past resource consumption data to suggest optimal CPU and memory allocations for containers. The VPA doesn't directly update resource configurations for existing pods. Instead, it identifies pods with incorrect configurations, terminates them, and allows their controllers to recreate them with the recommended settings.

In scenarios where both HPA and VPA are utilized simultaneously to manage container resources, conflicts may arise if they rely on the same metrics. This simultaneous attempt to address the situation can lead to incorrect resource allocations.

Coexistence is possible if HPA and VPA operate on different metrics. For example, if VPA utilizes CPU and memory consumption for precise resource allocation, HPA can be employed with custom metrics.

Node-Based Scaling

The Kubernetes Node Autoscaler complements the Horizontal and Vertical Pod Autoscalers by facilitating the scaling of cluster nodes in response to the number of pending pods. The Cluster Autoscaler (CA) regularly examines if there are pending pods and adjusts the cluster size accordingly.

It also efficiently deallocates idle nodes to maintain the cluster at its optimal size. In cases where resource limits are specified, the Node Autoscaler can initiate the deployment of new nodes directly into the pool, adhering to the defined resource constraints.

  • Cluster Upscaling

If pods are slated for execution and the Kubernetes Autoscaler identifies a potential resource shortage, it can dynamically increase the number of machines in the cluster. The diagram below provides a visual representation of how the cluster can undergo automatic upscaling:

Flow of Cluster Upscaling

The scenario depicted involves two pods scheduled for execution, but the current node's compute capacity has been reached. The cluster autoscaler systematically scans all nodes to assess the situation and triggers the provisioning of a new node under the following conditions:

  • Some pods have failed to schedule on existing nodes due to insufficient available resources.
  • The addition of a node with specifications identical to the current ones aids in redistributing the workload.
  • The cluster has not reached the user-defined maximum node count.

Once the new node is deployed and detected by the Kubernetes Control Plane, the scheduler assigns the pending pods to the cluster's fresh node. If there are pending pods, the autoscaler repeats the steps.

  • Cluster Downscaling

The Kubernetes Cluster Autoscaler reduces the count of nodes within a cluster when some nodes are deemed unnecessary for a predefined duration. A node is considered unnecessary if it exhibits low utilization, and all critical pods residing on it can be relocated to other nodes without causing a resource shortage.

The node scale-down evaluation considers the resource requests specified by the pods. If the Kubernetes scheduler determines that the pods can be relocated, it eliminates the node from the cluster to enhance resource utilization and minimize costs.

In cases where you have set a minimum threshold for the number of active nodes in the cluster, the autoscaler refrains from decreasing the node count below the specified threshold.

Autoscaling Best Practices In Kubernetes

There are some recommended practices for achieving seamless workload scaling in Kubernetes.

  1. Use An Up-To-Date Version Of The Autoscaler Object

    Kubernetes undergoes frequent updates with the addition of new features. It is advisable to use the compatible autoscaling object of the Kubernetes control plane version. This ensures that the cluster autoscaler effectively emulates the Kubernetes scheduler.

  2. Keep Requests Close To The Actual Usage

    Efficient scaling operations by the cluster autoscaler depend on accurate pod resource provisioning. Overprovisioning pod resources may lead to inefficient resource consumption or lower node utilization.

    To enhance performance, cluster administrators should align pod resource requests with historical consumption statistics. This helps ensure that each pod's resource requests closely match its actual usage trend.
  3. Retain Node Groups With Similar Capacity

    The cluster autoscaler assumes uniform memory and CPU resource capacity for every node within a node group. It creates a template node on which all cluster-wide scaling operations are performed. To ensure accurate performance, it is recommended to have node groups with nodes that share the same resource footprint.

  4. Define Resource Requests And Limits For Each Pod

    The autoscaler relies on node utilization and pod scheduling status for scaling decisions. Missing resource requests for pods can impact the calculation of node utilization, leading to suboptimal algorithm functioning. To ensure optimal scaling, administrators should define resource requests and limits for all pods running on a node.

  5. Specify Disruption Budgets For All Pods

    Kubernetes supports defining pod disruption budgets to manage the voluntary/involuntary disruption of workload replicas and prevent losses. Administrators should define disruption budgets to maintain a minimum threshold of pods. This ensures that the autoscaler optimally manages cluster services without exceeding the defined budget.


Navigating the complexities of Kubernetes autoscaling requires a strategic approach for optimal performance. Axelerant, with its expertise in cloud-native solutions, can help you navigate the intricacies of autoscaling in Kubernetes.

Whether it's Horizontal Pod Autoscaling (HPA), Vertical Pod Autoscaling (VPA), Node Autoscaler, or Cluster Autoscaler, our digital engineering experts ensure seamless integration tailored to your specific needs.

Schedule a meeting with our experts today and embark on a journey towards efficient, cost-effective, and automated Kubernetes scaling.

About the Author
Bassam Ismail, Director of Digital Engineering
About the Author

Bassam Ismail, Director of Digital Engineering

Away from work, he likes cooking with his wife, reading comic strips, or playing around with programming languages for fun.


Hanush Kumar, Marketing Associate

Hanush finds joy in YouTube content on automobiles and smartphones, prefers watching thrillers, and enjoys movie directors' interviews where they give out book recommendations. His essential life values? Positivity, continuous learning, self-respect, and integrity.

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