• Early Inspiration (1980s): While not exactly chaos engineering, a program called “Monkey” was created for the early Apple Macintosh that simulated chaotic user input. This playful experiment foreshadows the core idea of injecting randomness to test system robustness.
• Game Day at Amazon (Early 2000s): At Amazon, Jesse Robbins implemented “Game Day,” a practice of deliberately creating major failures in a controlled setting to improve website reliability. This approach drew inspiration from how firefighters train.
• Chaos Monkey at Netflix (2010): This is generally considered the birth of chaos engineering as a formal discipline. Facing the challenges of a complex cloud-based architecture with microservices, Netflix engineers created Chaos Monkey, a tool that randomly terminated virtual machines to test how the system would respond to such failures.
• The Simian Army and Open Source (2012): Netflix expanded on Chaos Monkey with the Simian Army, a suite of tools to inject various failures and simulate different types of disruptions [Wikipedia: Chaos Engineering]. In 2012, they open-sourced Chaos Monkey, allowing others to adopt this approach.
• The Rise of Chaos Engineering: Since then, chaos engineering has become a recognized practice for building more resilient systems. New companies offer tools specifically designed for chaos engineering, and the “Chaos Engineer” role has become more prominent.

Principles of Chaos Engineering

Chaos engineering relies on a set of core principles to guide its experiments and ultimately build confidence in a system’s ability to handle disruptions. Here are some key principles:

Understanding Steady-State Behavior:

• Define how the system should behave under normal conditions. This establishes a baseline for comparison when you introduce failures.
• Focus on measurable outputs rather than internal system functions.

Simulating Real-World Events:

• Design experiments that mimic real-world failures, like disk outages, network issues, or spikes in traffic.
• This helps identify weaknesses that might not be apparent in ideal circumstances.

Running Experiments in Production:

• While it might seem counter-intuitive, chaos engineering experiments are often run directly in production environments.
• This allows you to see how the system behaves under real-world load and configurations.

Automating and Minimizing Impact:

• Automate experiments to ensure they run regularly and catch potential issues before they cause problems.
• Minimize the “blast radius” of your experiments. This means isolating the affected components to avoid impacting the entire system or causing unnecessary disruption for users.

Here are some additional advanced principles you might encounter:

• Hypothesis-driven: Each experiment should test a specific hypothesis about how the system will react to a particular failure.
• Continuous Improvement: Chaos engineering is an ongoing process. Regularly iterate on your experiments and adapt them as your system evolves.

By following these principles, chaos engineering empowers you to proactively build fault tolerance and resilience into your systems.

User Personas Involved:

1. DevOps Engineers: These professionals are often responsible for deploying and maintaining software systems. They use chaos engineering to validate system resilience and identify potential points of failure.

2. Site Reliability Engineers (SREs): SREs focus on ensuring the reliability and performance of systems. They use chaos engineering to proactively identify and mitigate potential issues before they impact users.

3. Software Engineers: Engineers involved in building and maintaining software applications also benefit from chaos engineering. They use it to understand how their code behaves under different failure scenarios and to design more resilient software.

4. System Architects: Architects design the overall structure of systems and applications. They use chaos engineering to validate their architectural decisions and ensure that systems can handle failures gracefully.

Unlock Chaos Engineering powers with NetHavoc

NetHavoc offers a wide variety of chaos experiments across infrastructure, network, and application levels. In-built integration with Cavisson’s cutting-edge performance testing and observability solution makes it as simple as a matter of clicks to analyze the impact of chaos experiments with a production-level load on end-user experience by capturing actual user sessions and viewing the performance of your application.