Building an AWS Chaos Engineering Platform: Architecture, Experiments, and Real-World Resilience Testing
A production-ready AWS Chaos Engineering Platform that automates failure injection, blast radius control, resilience testing, GameDays, and observability. Built with serverless, Terraform, and AWS best practices to improve system reliability and fault tolerance.
Modern cloud systems are inherently complex. Microservices, distributed architectures, serverless components, containers, caching layers, and managed databases all work together to deliver high-availability applications. But complexity introduces fragility and traditional testing can’t uncover the failure modes that only emerge in real-world production traffic.
This is where Chaos Engineering comes in.
Inspired by Netflix’s Chaos Monkey, this project is designed to implement an automated chaos platform on AWS with capabilities such as:
- Intelligent failure injection
- Controlled blast radius
- GameDay support
- Automated chaos experiments
- Environment-aware safety controls
- Observability and auto-rollback
While the repository is currently documentation-heavy, it forms a complete architectural foundation for a scalable, production-ready Chaos Engineering Platform.
What This Platform Aims to Solve
Organizations often struggle with:
- Hidden single points of failure
- Non-resilient components in distributed systems
- Over-engineered but under-tested DR strategies
- Auto-scaling policies that work only on paper
- Incidents caused by unpredictable interactions between services
This platform aims to solve those challenges by constantly validating system resilience, not just during outages or periodic load tests.
It brings together:
✔ Chaos Monkey principles
✔ AWS-native automation
✔ Infrastructure as Code (IaC)
✔ Monitoring & observability
✔ CI/CD pipelines
✔ Enterprise security
High-Level Architecture
Here is a simplified high-level architecture diagram illustrating the core components of the platform:
This architecture reflects real-world enterprise setups supporting hybrid compute, databases, caching layers, and a deep observability pipeline.
Deep-Dive into System Components
1. Chaos Controller
The orchestration brain of the platform:
- Decides when experiments run
- Selects experiments based on rules/policies
- Applies safe “blast radius” constraints
- Logs decisions for auditing
2. Chaos Experiment Engine
Runs failure injection logic such as:
- EC2 instance termination (Chaos Monkey)
- RDS failover
- Induced Lambda throttling
- ECS task kill
- Cache invalidations
- Network latency simulation (via SSM)
This is typically deployed as AWS Lambda or a lightweight ECS microservice.
3. Observability & Feedback Loop
Chaos Engineering is incomplete without strong monitoring.
The platform integrates:
- CloudWatch Metrics
- CloudWatch Logs
- AWS X-Ray (trace-level insights)
- SNS Alerts (email, Slack, PagerDuty)
- Anomaly detection
These ensure every chaos event generates actionable insights.
Data Flow: How a Chaos Experiment Runs
- EventBridge triggers a scheduled chaos job
- Chaos Controller validates safety policies
- Authorization & scope checks run
- Chaos Experiment Engine selects a failure mode
- Failure is injected into the target AWS service
- Metrics & traces are collected
- Alerts fire if thresholds exceed risk limits
- Rollback or cooldown logic is applied
Clear, predictable, and with guardrails.
Deployment Pipeline (Dev → Staging → Canary → Prod)
The platform includes a modern CI/CD workflow using GitHub Actions:
- Develop locally → push to GitHub
- Pipeline runs tests + validation
- Staging deployment for pre-prod testing
- Canary rollout with 10% traffic
- Automatic monitoring verifies:
- error rate
- latency
- failure propagation
- Full production deployment
- Automatic rollback on anomalies
This aligns with AWS Well-Architected best practices.
Enterprise-Grade Security Architecture
Security is baked into the system:
- IAM least privilege roles
- Secrets Manager for credentials
- TLS 1.3 in transit, KMS at rest
- VPC isolation private subnets for compute
- WAF and rate-limiting protection
- Automated security scanning
- CloudTrail auditing for every API call
Compliance frameworks covered:
- SOC 2
- PCI-DSS
- HIPAA
- GDP
Key Platform Features
✔High Availability & Resilience
Multi-AZ deployments, autoscaling, and auto-failover ensure reliability even during chaos.
✔ Automated Chaos Experiments
Scheduled or manual failure injections across compute, storage, networking, and databases.
✔ Monitoring & Observability
Full telemetry via CloudWatch, X-Ray, and VPC Flow Logs.
✔ Infrastructure as Code
Terraform/CDK provisioning: reproducible, scalable, and version-controlled.
✔ Disaster Recovery Support
Built-in backups, PITR, multi-AZ and multi-region failover.
✔ Cost Optimization
Right-sizing, Spot fleets, idle resource cleanup, S3 tiering, and cost allocation tags.
Cost Breakdown (Development vs Production)
| Component | Dev Environment | Production |
|---|---|---|
| Compute | $100–300 | $500–2000 |
| Databases | $50–150 | $200–1000 |
| Storage | $20–50 | $100–500 |
| Monitoring | $10–20 | $50–200 |
| Networking | $10–30 | $50–300 |
| Total | $190–550 | $900–4000 |
Key savings tools:
- Spot instances
- S3 Intelligent-Tiering
- Scheduled environment shutdown
- Reserved Instances for predictable workloads
Final Thoughts
This Chaos Engineering Platform is a powerful, AWS-native, highly extensible design for building resilience into cloud systems. Even in its documentation-first form, the repository demonstrates:
- Clear architecture
- Production-ready patterns
- Strong cloud engineering mindset
- Comprehensive observability & security model
With implementation, this can evolve into a full-fledged platform comparable to commercial chaos tools.
Repository: https://github.com/rahulladumor/chaos-engineering-platform
Project: Chaos Engineering Platform – AWS Chaos Monkey Implementation
License: MIT
