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Infrastructure Testing in Kubernetes

Published
July 20, 2025
Atulpriya Sharma
Sr. Developer Advocate
Improving

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Last updated
July 19, 2025
Atulpriya Sharma
Sr. Developer Advocate
Improving
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Table of Contents

Kubernetes Infrastructure Testing TL;DR

TL;DR

Kubernetes Infrastructure Testing Essentials

  • 1
    Infrastructure testing is critical - Kubernetes complexity means failures in networking, storage, or nodes can cause complete outages that impact revenue and customer trust
  • 2
    Test four key areas - Focus on cluster health (API server, etcd, nodes), compute resources (CPU/memory/GPU), networking (DNS, services, ingress), and storage (PVCs, throughput, backups)
  • 3
    Time your tests strategically - Run infrastructure tests pre-deployment, post-deployment, after upgrades, at scheduled intervals, and before critical releases
  • 4
    Testkube simplifies the process - This cloud-native framework unifies multiple testing tools (Curl, k6, Pytest) into Kubernetes-native workflows without complex integration overhead
  • 5
    Practical examples included - The article shows real TestWorkflow configurations for cluster health checks, network validation, and GPU testing using familiar tools like Curl

Kubernetes has become the de facto container orchestrator that enables organizations to build scalable, available, and resilient apps that meet modern business demands. However, with this comes complexity. Infrastructure failures, from network routing to node failures and storage failures, can lead to significant business disruptions. A single misconfigured policy or resource quota can lead to a complete outage, directly impacting the revenue and customer trust. 

That is where Infrastructure testing in Kubernetes becomes crucial. By validating Kubernetes components, teams can prevent downtimes, ensure compliance, and maintain high availability. Thorough testing of various components and layers helps identify issues before they reach production, reducing mean time to recover (MTTR) and improving overall reliability.

In this post, we’ll examine infrastructure testing strategies for Kubernetes and discuss ways and best practices for testing infrastructure to ensure production readiness.

Key Areas of Infrastructure Testing

When we talk about infrastructure testing in Kubernetes, we’re talking about the foundation that supports your entire application ecosystem. Beyond just checking if the pods are running, it involves comprehensive testing of cluster health, compute, networking, and storage resources. Such an all-around approach ensures that your infrastructure is ready to support your production workloads while keeping up with security and performance standards. 

Cluster Availability and Health

This serves as the foundation for reliable Kubernetes operations. Effective testing requires validating the control plane components - API server responsiveness, scheduler functionality, and controller manager operations - and ensuring these meet the SLAs. You must also validate node readiness to confirm kubelet health and capacity for scheduling your workloads. 

You should also focus on validating etcd to ensure that core configurations are intact to prevent data corruption. You should also check add-on services like CoreDNS, kubeproxy, and any other tool you use to identify warning signals early and avoid cascading failures. 

Compute Resource Validation

Ensuring your cluster can effectively manage and distribute workloads is critical. It is important to verify the node capacity metrics including CPU, memory and storage while confirming the allocatable resources after system reservations. For specialized workloads like AI & ML, you need to validate device plugins and compatibility for GPU/TPU resources. 

Resource quota enforcement testing ensures namespace level constraints, validating the limits during load conditions and ensuring appropriate throttling behaviors. It’s also important to validate node affinity and taint configurations for correct pod placement to maintain workload isolation and ensure critical workloads get adequate resources. 

Network Validation

Testing networks is critical for application connectivity. DNS resolution tests must be done to confirm both internal kube-dns and external name resolution are working as expected with an appropriate timeout. Service resolution testing ensures ClusterIP, NodePort, or LoadBalancers services function correctly across namespaces. 

Test your ingress to confirm correct rule processing, TLS termination, and backend service routing under various load conditions. Validate network policies for proper pod isolation. You should also check the load balancer configuration to ensure proper health probe functionality, session persistence, and correct traffic distribution. 

Storage Validation

Validating storage ensures data persistence and availability for stateful workloads in Kubernetes environments. Storage class availability testing verifies provisioner functionality and validates reclaim policies across different storage backends. PVC provisioning tests must validate capacity limits, access modes and correct volume attachment to nodes.

Measuring throughput and latency under various load conditions is also critical for ensuring I/O profiles meet application requirements. This will also identify any bottlenecks during concurrent operations. Lastly, validating backups is critical for smooth operations. Check for snapshot creation capabilities, check restoring procedures with data integrity verification, and validate retention policy enforcement.

When To Run Infrastructure Tests

While we now know the importance of infrastructure testing in Kubernetes, knowing precisely when to execute infrastructure tests can reduce operational risks. Infrastructure failures can impact multiple workloads simultaneously. By knowing when to execute your infrastructure tests, you can prevent any cascading failures and reduce debug times.

Below are some strategic points when you should run your infrastructure tests:

  • Pre-deployment: validate infrastructure and cluster health before workloads are deployed.
  • Post-deployment: After the workload is deployed and there are any changes done using tools like Terraform or CloudFormation
  • Post-upgrade: After the cluster is upgraded, verify compatibility.
  • Scheduled intervals: Regular infrastructure tests to detect any configuration drift.
  • Before critical releases: Conduct pre-flight checks before deploying critical workloads.

The scope of testing and frequency should align with your management practices along with a comprehensive testing plan.

Implementing Infrastructure Testing with Testkube

Infrastructure testing in Kubernetes requires validating multiple components across cluster health, compute, networking, and storage. This often demands multiple testing tools and methods, many of which weren’t designed for container environments. Traditional approaches create integration challenges and add to management overhead. 

That’s where Testkube comes in. It addresses these challenges by providing a cloud-native testing framework that unifies infrastructure testing workflows. Let's understand how Testkube helps.

Testkube is a cloud-native, vendor-agnostic test execution framework that makes testing in Kubernetes straightforward. Using Testkube, you can orchestrate and automate complex validation workflows using different testing tools while maintaining Kubernetes-native operations.

Benefits of Using Testkube For Infrastructure Testing

Testkube enhances infrastructure testing by integrating seamlessly with Kubernetes, allowing teams to leverage their full potential. Here are a few benefits of using Testkube:

  • Orchestrate Complex Test Workflow: Test Workflows enable sequential and parallel test executions that mimic real-world scenarios without complex scripting, giving you better control over infrastructure validation.
  • Seamless integration: Testkube effortlessly articulates with CI/CD platforms like Jenkins, GitLab CI, and Argo CD, allowing for automated infrastructure testing as part of the deployment process.
  • Integration with Testing Tools: Testkube works with popular testing tools like k6, Curl, and Postman, allowing you to combine different approaches for comprehensive infrastructure validation.
  • Single pane of Glass: All test results, logs, and artifacts are in one place, no matter how tests are triggered, and they provide streamlined test troubleshooting and powerful holistic reporting

Thanks to its flexibility and alignment with cloud-native technologies, Testkube allows you to execute test workflows at various strategic points in your application's lifecycle:

Infrastructure Testing Using Testkube

By orchestrating tests that leverage familiar tools like Curl, Pytest, and K6, Testkube eliminates integration challenges without compromising text execution. Below are a few examples demonstrating how Testkube makes complex infrastructure testing accessible and efficient for production environments.

Using Curl for Cluster Health Checks and Resource Validations

Testkube makes it easy to implement comprehensive cluster health checks using simple HTTP-based tools like Curl. This validates control plane components, etcd health, and node readiness in a Kubernetes-native way.

apiVersion: testworkflows.testkube.io/v1
kind: TestWorkflow
metadata:
	name: cluster-health-validation
    namespace: testkube
spec:
	steps:
    - name: Validate API Server Health
    container:
    	image: curlimages/curl:8.7.1
        shell: curl -k https://kubernetes.default.svc/healthz
    - name: Check Node Readiness
    container:
    	image: curlimages/curl:8.7.1
        shell: curl -k https://kubernetes.default.svc/api/v1/nodes | grep -i ready
    - name: Validate Etcd Health
    container:
    	image: curlimages/curl:8.7.1
        shell: curl -k https://kubernetes.default.svc/healthz/etcd
     - name: Check CoreDNS Status
     container:
     	image: curlimages/curl:8.7.1
        shell: "curl -k https://kubernetes.default.svc/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy/health"

This workflow validates critical cluster components by sequentially checking API server responsiveness, confirming node readiness status, verifying etcd health, and ensuring CoreDNS is working as expected.

Using cURL for Network Testing

Network testing requires validating critical communication paths in your Kubernetes cluster. Here's a simple example using cURL to verify ingress controller health:

kind: TestWorkflow
apiVersion: testworkflows.testkube.io/v1
metadata:
	name: ingress-health-check
    namespace: testkube
    spec:
    	content:
        	files:
            - path: /data/check_ingress.sh
            content:|-
            #!/bin/bash
            set -e
            
            # Test Ingress controller health
            echo "Testing Ingress controller..."
            ingress_status=$(curl -s -o /dev/null -w "%{http_code}" http://ingress-nginx-controller.ingress-nginx.svc.cluster.local:10254/healthz)
            if [ "$ingress_status" == "200" ]; then
            	echo "✅ Ingress controller is healthy"
            else
            	echo "❌ Ingress controller check failed with status $ingress_status"
                exit 1
               fi
           steps:
           - name: Check Ingress Health
           workingDir: /data
           run:
           	image: curlimages/curl:8.3.0
            command: ["/bin/sh", "-c"]
            args:
            - |
            chmod +x /data/check_ingress.sh
            /data/check_ingress.sh‍

This workflow focuses on validating your ingress controller's health - a critical component for external traffic routing.

Using Node Selectors for GPU Test

Hardware validation ensures that specialized workloads have the resources they need. This example tests GPU availability and performance.

apiVersion: testworkflows.testkube.io/v1
kind: TestWorkflow
metadata:
	name: nvidia-gpu-workflow
    namespace: testkube
spec:
container:
	resources:
    	limits:
        	nvidia.com/gpu: 1
            memory: 200Mi
            cpu: 100m
        requests:
        	nvidia.com/gpu: 1
            memory: 200Mi
            cpu: 100m
pod:
	nodeSelector:
    	cloud.google.com/gke-accelerator: nvidia-tesla-t4
   steps:
   	- name: Run test
    run:
    	image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2‍

The above test workflow has a single step that executes a Docker image which does matrix calculations using CUDA. The test will fail if the Pod running it does not run on a Node with a GPU or if the CUDA Toolkit is not installed.

What we saw here were a few examples of how you can use Testkube for Infrastructure testing in Kubernetes. You can build more complex workflows to suit your requirements. Check out our blog posts to learn more about building complex workflows using Testkube. 

Summary

Infrastructure testing in Kubernetes is complex due to the presence of multiple components, resources, networking, and storage. Effective validation of all of these requires a comprehensive approach using multiple tools, each targeting different aspects of infrastructure testing. 

Testkube addresses this complexity by providing a cloud-native and vendor-agnostic testing framework that unifies different tools into cohesive workflows. It helps teams implement thorough infrastructure validation with minimal operation overhead. 

We would love to hear all about the custom test workflows that you created using Testkube for infrastructure testing. If you face any issues, remember that the entire Testkube team, plus a vibrant community of fellow Kubernetes testers, are on Slack. We’re just getting started in building the most comprehensive cloud-native testing framework for Kubernetes so feel free to follow us on X @testkubeio.

Top 5 Most Important Infrastructure Testing in Kubernetes FAQs

Kubernetes Infrastructure Testing FAQs

Essential questions about testing infrastructure in Kubernetes environments

Infrastructure testing in Kubernetes refers to validating the underlying components that support workloads—such as cluster health, compute, networking, and storage. This type of testing ensures that the foundational elements of your Kubernetes environment are functioning correctly before and during application deployments.

Infrastructure testing is crucial for several reasons:

  • Ensuring availability: Validates that critical cluster components are operational and ready to support workloads
  • Preventing misconfigurations: Catches configuration issues that could lead to service disruptions or security vulnerabilities
  • Reducing downtime: Identifies infrastructure problems before they impact production applications
  • Catching issues early: Detects problems in staging environments to prevent them from reaching production
  • Maintaining reliability: Ensures consistent performance and stability of the underlying platform

By validating infrastructure components proactively, teams can maintain confidence in their Kubernetes platform and prevent cascading failures that affect multiple applications.

Infrastructure tests should be run strategically during key lifecycle events to maximize their effectiveness:

  • Before and after deployments: Validate that the infrastructure is ready to support new workloads and remains stable afterward
  • After infrastructure upgrades: Ensure that cluster updates, node additions, or configuration changes haven't introduced issues
  • At scheduled intervals: Regular automated testing to catch degradation or drift over time
  • Before critical releases: Extra validation before high-impact deployments to minimize risk
  • During incident response: Quick validation of infrastructure health during troubleshooting
  • After scaling events: Verify that autoscaling operations haven't affected cluster stability

This timing strategy helps:

  • Reduce the risk of cascading failures across multiple services
  • Ensure the environment is production-ready before deploying applications
  • Maintain continuous confidence in infrastructure reliability
  • Enable faster root cause analysis when issues occur

Several tools are commonly used for different aspects of Kubernetes infrastructure testing:

  • Curl: Essential for API and health checks
    • Simple HTTP endpoint validation
    • Service connectivity testing
    • Quick health check scripting
  • k6: Modern tool for load and performance testing
    • Infrastructure stress testing
    • Resource limitation validation
    • Network performance measurement
  • Postman: User-friendly API testing platform
    • Comprehensive API test suites
    • Environment-specific configurations
    • Automated test collections
  • Pytest or Bash scripts: Custom validation frameworks
    • Cluster-specific test logic
    • Integration with existing workflows
    • Flexible test automation

Tools are often orchestrated together using frameworks like Testkube for Kubernetes-native execution and reporting, providing centralized test management and result aggregation across multiple testing tools and scenarios.

A comprehensive Kubernetes infrastructure testing strategy should validate all critical components:

  • Cluster health: Core control plane and cluster functionality
    • API server responsiveness and availability
    • etcd cluster status and performance
    • Node health and readiness status
    • CoreDNS functionality and resolution
  • Compute resources: Processing and memory capabilities
    • CPU and memory quota enforcement
    • Resource limits and requests validation
    • GPU/TPU availability and allocation
    • Node capacity and scheduling
  • Networking: Connectivity and service discovery
    • DNS resolution within the cluster
    • Ingress controller functionality
    • Service-to-service communication
    • Network policy enforcement
  • Storage: Persistent data and volume management
    • PVC provisioning and binding
    • Storage class functionality
    • I/O performance benchmarks
    • Backup and restore capabilities

Automation of infrastructure testing can be achieved by integrating testing into CI/CD workflows using tools like Testkube:

  • Define test workflows as Kubernetes resources:
    • Create Test and TestSuite custom resources
    • Store test configurations in version control
    • Apply GitOps principles to test management
  • Trigger tests with multiple mechanisms:
    • GitOps workflows for continuous testing
    • Webhooks for event-driven execution
    • Cron jobs for scheduled validation
    • Manual triggers for on-demand testing
  • Aggregate results in a single dashboard:
    • Centralized monitoring and alerting
    • Historical trend analysis
    • Integration with existing observability tools
    • Automated notification on test failures
  • Integration benefits:
    • Consistent test execution across environments
    • Reduced manual testing overhead
    • Faster feedback loops for infrastructure changes
    • Improved reliability through continuous validation

About Testkube

Testkube is a test execution and orchestration framework for Kubernetes that works with any CI/CD system and testing tool you need. It empowers teams to deliver on the promise of agile, efficient, and comprehensive testing programs by leveraging all the capabilities of K8s to eliminate CI/CD bottlenecks, perfecting your testing workflow. Get started with Testkube's free trial today.