Naga Ravi Chaitanya Elluri
Ravi Elluri leads Chaos Engineering efforts at Red Hat, with a focus on enhancing the resilience, performance, and scalability of Red Hat’s product portfolio. He plays a key role in hardening products by testing pre-release versions and ensuring stable, production-ready releases for customers. Ravi has worked closely with large enterprise clients, particularly in the financial and airline sectors, to help them implement chaos testing pipelines at scale, enabling successful production deployments.
He also collaborates with the IBM Research team on AI-driven use cases and represents Red Hat in the open-source and CNCF communities, advocating for chaos engineering best practices.
Red Hat
Job title –Team Lead and Principal Software Engineer
Sessions
Are Your Pipeline Tests Ready for High-Stakes Deployments?
When deploying on Fridays in a large, complex Kubernetes environment, how confident are you that your pipeline tests will catch issues before they impact users? And more importantly, what happens if disaster strikes?
Join us as we demonstrate how to use Krkn, an open-source CNCF chaos testing framework, to uncover bottlenecks in a high-density Kubernetes deployment. We’ll show you how to stress-test your system, simulate failure scenarios, and ensure your services perform as expected even under chaos. You’ll also learn how to define and measure Service Level Objectives (SLOs) to validate your system’s health and performance during and after chaos testing, guaranteeing a resilient and smooth user experience.
Key Takeaways:
Complex Chaos Workflows: Trigger chaos tests across diverse cloud environments via lightweight binary.
Lessons from Large-Scale Testing: Real-world challenges and best practices from chaos testing on Kubernetes clusters with over 250 nodes.
Performance Under Stress: Establish and validate SLOs to ensure your system meets both health and performance metrics during chaos scenarios.
Automated Chaos Scenarios: Leverage the Chaos-Recommender tool for AI-driven suggestions on testing scenarios.
AI-Powered Insights: Discover how AI can optimize test coverage, improve chaos resilience, and streamline testing workflows.
By the end of the session, you’ll walk away with a comprehensive chaos testing guide that you can apply directly to your own infrastructure and application stack, ensuring that you’re prepared for the unexpected.
Are Your Pipeline Tests Ready for High-Stakes Deployments?
When deploying on Fridays in a large, complex Kubernetes environment, how confident are you that your pipeline tests will catch issues before they impact users? And more importantly, what happens if disaster strikes?
Join us as we demonstrate how to use Krkn, an open-source CNCF chaos testing framework, to uncover bottlenecks in a high-density Kubernetes deployment. We’ll show you how to stress-test your system, simulate failure scenarios, and ensure your services perform as expected even under chaos. You’ll also learn how to define and measure Service Level Objectives (SLOs) to validate your system’s health and performance during and after chaos testing, guaranteeing a resilient and smooth user experience.
Key Takeaways:
Complex Chaos Workflows: Trigger chaos tests across diverse cloud environments using simple YAML configurations.
Lessons from Large-Scale Testing: Real-world challenges and best practices from chaos testing on Kubernetes clusters with over 250 nodes.
Performance Under Stress: Establish and validate SLOs to ensure your system meets both health and performance metrics during chaos scenarios.
Automated Chaos Scenarios: Leverage the Chaos-Recommender tool for AI-driven suggestions on testing scenarios.
AI-Powered Insights: Discover how AI can optimize test coverage, improve chaos resilience, and streamline testing workflows.
By the end of the session, you’ll walk away with a comprehensive chaos testing guide that you can apply directly to your own infrastructure and application stack, ensuring that you’re prepared for the unexpected.