Authors: Sarvi Aryanpour and Muktha Ala
In today’s always-on enterprise environments, unexpected system failures and downtime can significantly impact business continuity and customer trust. IBM Software Hub, a cloud-native enterprise platform built on Red Hat OpenShift, supports critical services across multiple domains, making reliability a top priority. To ensure the platform can gracefully handle failures such as pod crashes, resource saturation, or network disruptions, we have integrated chaos engineering into our validation process using Chaos Mesh, a Kubernetes-native chaos orchestration tool. Its high level of automation and flexibility allows us to simulate real-world failure scenarios in a controlled and measurable way. This proactive testing approach enables us to validate how IBM Software Hub behaves under failure conditions, uncover potential weaknesses early, and continuously strengthen system resilience.
Why conduct Chaos Testing for IBM Software Hub?
Cloud-native platforms like IBM Software Hub are complex, composed of distributed micro-services that scale dynamically across hybrid environments. While Red Hat OpenShift offers built-in fault tolerance, issues in one service can cascade and impact others if not handled effectively.
Chaos testing allows us to:
- Validate the platform’s self-healing and failover capabilities (e.g., pod restarts and node re-balancing)
- Test responses to real-world failure scenarios like memory exhaustion or network degradation
- Validate alerts, observability, and response times
- Build confidence in the platform's stability before reaching production
Our Chaos Testing Approach
We use the following structured and repeatable methodology to execute chaos experiments across the IBM Software Hub stack:
1. Prepare Environment and Establish a Baseline
We begin with a healthy and stable IBM Software Hub deployment. Platform and key services must be operational to establish a baseline for impact assessment.
We install Chaos Mesh in a dedicated namespace with the required RBAC settings to ensure safe and isolated execution of chaos experiments. Helm is our preferred deployment tool. Chaos Mesh includes a dashboard that offers a visual interface to design, schedule, and monitor chaos scenarios collaboratively. For more information on how to install and setup Chaos Mesh, refer to the References section.
3. Design Failure Scenarios
We design chaos experiments to simulate common failure patterns observed in field incidents or stress-testing environments. These experiments are then captured and exported into YAML files, which can be customized with parameters such as the target namespace or service addOnId and attack-specific variables. The YAML files are subsequently integrated into our automated chaos testing pipeline for consistent and repeatable execution.
Some examples of the attacks include:
- PodChaos: Terminate key service pods to verify if IBM Software Hub auto-recovers as expected.
- NetworkChaos: Simulate latency, DNS failures, or packet loss between services to observe system degradation and resilience.
- StressChaos: Induce specified CPU or memory pressure on targeted workloads to evaluate performance under constrained resources.
- Storage Full Simulation: Emulate full disk conditions on worker nodes or pods to test log retention, cleanup strategies, and application behavior when write operations fail.
Example of Stress Chaos attack YAML file
Example of Network Chaos (packet drop) attack YAML file
4. Observe and analyze results
During each experiment, we collect metrics via Prometheus, inspect system logs, and leverage internal cluster health-check diagnostics. We monitor:
- Time to recovery (TTR)
- Service uptime and functional integrity
- End-user experience degradation
This data provides actionable insights to improve service design, automation, and recovery logic for future releases.
Fix weak recovery mechanisms; enhance health probes and failover logic etc., and document possible recovery workarounds in the IBM Knowledge Center.
Chaos scenarios are continuously integrated into our testing pipelines. This ensures new deployments maintain the same level of reliability and previously resolved issues do not reemerge.
Chaos Test Advantages from a Quality Perspective
Chaos testing significantly boosts the overall quality of IBM Software Hub by addressing core attributes of reliable software:
- Reliability: Validates the platform’s ability to recover from failures and maintain service continuity under stress.
- Observability: Tests monitoring and alerting systems by forcing abnormal states, helping teams detect and respond to real incidents faster.
- Early Defect Detection: Exposes hidden bugs and architectural weaknesses in a controlled environment, improving issue resolution before production.
- Failover Confidence: Confirms that health checks, restart logic, and recovery mechanisms work as intended.
- Regression Prevention: Automated chaos scenarios ensure previously resolved issues don’t reappear in future releases.
By integrating Chaos Mesh into our validation process, we’ve adopted a proactive approach to resilience engineering in IBM Software Hub. Simulating real-world failures helps uncover issues early, validate recovery mechanisms, and ensure that the platform remains robust before production deployment. In today’s complex cloud-native environments, chaos testing plays a key role in delivering reliable, enterprise-grade solutions and should be considered a vital part of any validation strategy.