Chaos Engineering Setup
You do not know your system is resilient until you have tested it under failure conditions. Anubiz Engineering implements chaos engineering — controlled fault injection experiments that reveal weaknesses before they cause real outages. We start small, scope experiments carefully, and build confidence in your system's failure handling.
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Experiment Design
Every chaos experiment starts with a hypothesis: "If we kill 30% of API pods, latency stays under 500ms because the autoscaler replaces them within 60 seconds." We design experiments around your actual failure modes — network partitions, pod evictions, disk pressure, DNS failures, dependency timeouts — not random destruction. Each experiment has a clear blast radius and abort criteria.
Tooling and Infrastructure
We deploy Litmus Chaos, Chaos Mesh, or Gremlin depending on your environment. For Kubernetes workloads, we use native chaos operators. For VM-based infrastructure, we use agent-based fault injection. Each tool integrates with your observability stack so experiment impact is visible in real-time dashboards alongside production metrics.
Staged Rollout
Chaos in production starts after chaos in staging. We run experiments in development first, then staging, then production during low-traffic windows, and finally production during peak. Each stage validates the previous findings and builds team confidence. The first production experiment is always the smallest possible blast radius.
Continuous Chaos
Mature chaos engineering is not a one-time exercise. We set up automated chaos experiments that run on schedule — weekly pod kills, monthly network partition tests, quarterly full zone-failure simulations. Results feed into your SLO tracking. If an experiment causes SLO breach, it generates a postmortem and action items just like a real incident.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.