Real scenario: Bad deploy on Friday PM. Unhandled exception crashes services. Memory leak grows. Cascading failures. Threats: bugs, crashes, mem leaks. Best tools: Docker (isolation), K8s (auto-restart). Lesson: Contain failures before they cascade.
🔴 SECURITY BREACH
Real scenario: Attacker scanning firewall. DDoS attempt. Brute-force credentials. Port probing. Threats: DDoS, intrusions, auth attacks. Best tools: Firewall (network defense), Docker (service isolation). Lesson: Perimeter defense stops most attacks.
🌀 FULL OUTAGE
Real scenario: Perfect storm: Traffic spike + unexpected bug + security probe. All threats escalate. Requires: Mixed strategy with all 4 tools. Lesson: Diverse resilience survives compound failures.
THREAT BESTIARY (LEARN EACH INCIDENT)
LOAD THREATS — happen when systems receive more work than they can handle
📈 Traffic Spike = sudden 10× traffic in microseconds. Connections exhaust. Timeouts. Example: Someone tweets your service.
🌊 Traffic Flood = sustained high load for minutes. Exhausts both network and CPU. Example: Server reaches front page of news.
⏱ Request Timeout = requests hang, threads block. Service becomes unresponsive but still running. Example: Database query takes 30s instead of 100ms.
BUG THREATS — happen when code has logical errors or resource leaks
🐛 Runtime Bug = unhandled exception in production code. Crashes one request, isolates to one pod in containers. Example: null pointer in user API.
💥 Service Crash = critical bug or OOMKilled. Whole service dies. Example: Infinite loop consuming all RAM, OS kills process.
💾 Memory Leak = process slowly consumes RAM without releasing it. Eventually OOMKilled. Example: Event listener never unregistered, grows every request.
SECURITY THREATS — happen when attackers or misconfigurations expose the system
💣 DDoS Attack = attacker floods with millions of fake requests. Network layer can't tell them apart from real users. Firewall rate-limits at source.
🕵️ Intrusion = attacker scans open ports, probes for vulnerabilities. Firewall IDS/IPS (Intrusion Detection System) detects and blocks suspicious patterns.
🔓 Auth Attack = credential stuffing, brute force, or bad login tokens. Firewall can rate-limit. Docker isolates compromised auth service.
DEVOPS CONCEPTS (WHAT YOU'RE LEARNING)
SLO (Service Level Objective) = percentage of time your service is UP and responding. 99% SLO = 7.2 hours downtime allowed per month. Each PROD breach = SLO violation. Goal: keep 100% uptime, or SLO% as high as possible. MTTR (Mean Time To Recovery) = average time from incident start to full resolution. This game tests your MTTR. Fast response = lower MTTR = happier users. RESILIENCE = how well your system survives failures without going down. 4 core resilience patterns: elasticity (scale up), isolation (contain failures), healing (auto-recover), redundancy (multiple paths). OBSERVABILITY = logs, metrics, traces. This game shows you: threat type, effectiveness, SLO, HP. Real incident response = same: understand the failure, apply the fix. INCIDENT RESPONSE WORKFLOW = (1) Detect threat, (2) Classify incident type, (3) Deploy tools matched to threat, (4) Monitor recovery, (5) Post-mortem. This game = entire workflow in 5 minutes.
REAL TOOL MAPPING
🔥 FIREWALL (physical/cloud networking device) = AWS Security Groups, GCP Firewall, iptables. Operates at Layer 3-4 (network/transport). Blocks IPs, drops malicious packets before they reach services. IDS/IPS (Intrusion Detection/Prevention) = firewall with AI learning. 🐳 DOCKER (containerization platform) = Linux containers, cgroup resource limits, process isolation. If one container crashes, others keep running. Can restart crashed container instantly. Used by every production Kubernetes cluster. ☁️ AWS LAMBDA (serverless compute) = auto-scaling functions, handled by cloud provider. When traffic spikes, provider spins up thousands of function copies. No servers to manage. Alternative: Google Cloud Run, Azure Functions. ⎈ KUBERNETES (container orchestration) = manages 1000s of Docker containers across 100s of machines. Auto-restarts failed pods, redistributes load, manages rolling updates. Industry standard for production container orchestration.
KEYBOARD SHORTCUTS
SPACE Start next wave
P Pause / resume
ESC Deselect / close inspect
S Sell mode
1 2 3 Speed ×1/×2/×3
Q Firewall
W Docker
E AWS Lambda
R Kubernetes
SCENARIO STRATEGY
• TRAFFIC SURGE → build AWS Lambda first (AOE absorbs spikes) + K8s for sustained load.
• BUG INFESTATION → Docker at chokepoints contains crashes. K8s auto-heals PROD.
• SECURITY BREACH → Firewall stops DDoS/intrusions at the entry point. Docker isolates escaping threats.
• FULL OUTAGE → You need all 4 tools. Place in zones by threat type shown in the wave preview.
Traffic surge happens when a system receives sudden spikes in requests. In Black Friday, traffic can jump 10-100x normal load instantly. Systems must auto-scale to handle it.
▶How Auto-scaling Works
Auto-scaling automatically adds more compute resources when demand increases. Lambda scales by running more function instances. Kubernetes scales by adding more pod replicas.
▶Why Elastic Compute Matters
Elasticity is a key resilience pattern. Fixed infrastructure fails under load. Elastic systems scale automatically, preventing downtime and protecting revenue during traffic spikes.
▶Kubernetes in Action
Kubernetes is an orchestrator that manages containers at scale. It can auto-scale pods based on CPU/memory usage, perform rolling updates, and self-heal failed pods.
▶Real-world Example
2012 Black Friday: AWS Lambda and Kubernetes customers handled 50× normal traffic. Systems without auto-scaling crashed. Cost: millions in lost revenue for unprepared companies.
▶Common Pitfalls
❌ No monitoring = hidden failures
❌ Single point of failure
❌ Not testing failure scenarios
❌ Ignoring cost of scaling
✓ Always plan for 10x traffic!