🔧 System Components¶

Core Components Overview¶

The self-healing infrastructure consists of several key components that work together to provide a resilient, automated system.

Component Architecture¶

graph TB
    subgraph "Infrastructure Layer"
        TF[Terraform]
        K8S[Kubernetes]
        STOR[Storage]
    end

    subgraph "Platform Layer"
        MON[Monitoring]
        LOG[Logging]
        SEC[Security]
    end

    subgraph "Application Layer"
        APP[Applications]
        CHAOS[Chaos Engine]
        HEAL[Self-Healing]
    end

    subgraph "Integration Layer"
        CI[CI/CD]
        API[APIs]
        WEB[Web UI]
    end

    TF --> K8S
    K8S --> MON
    K8S --> LOG
    K8S --> SEC
    K8S --> APP
    K8S --> CHAOS
    K8S --> HEAL
    CI --> K8S
    API --> APP
    WEB --> MON

1. Infrastructure Components¶

Terraform Infrastructure¶

Purpose: Infrastructure as Code for provisioning and managing cloud resources

Key Features: - Declarative infrastructure definition - Version-controlled infrastructure changes - Automated resource provisioning - Cost optimization and management

Configuration:

# Infrastructure modules
module "kubernetes_cluster" {
  source = "./modules/kubernetes"

  cluster_name = "self-healing-cluster"
  node_count   = 3
  node_type    = "t3.medium"

  tags = {
    Environment = "production"
    Project     = "self-healing-infrastructure"
  }
}

module "monitoring_stack" {
  source = "./modules/monitoring"

  prometheus_retention = "15d"
  grafana_admin_password = var.grafana_password

  depends_on = [module.kubernetes_cluster]
}

Kubernetes Cluster¶

Purpose: Container orchestration and management platform

Components: - Control Plane: API server, scheduler, controller manager - Worker Nodes: Application pods and workloads - etcd: Distributed key-value store - kubelet: Node agent for pod management

Configuration:

# Cluster configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-config
  namespace: kube-system
data:
  cluster-name: "self-healing-cluster"
  environment: "production"
  auto-scaling: "enabled"
  monitoring: "enabled"

2. Monitoring Components¶

Prometheus¶

Purpose: Metrics collection, storage, and querying

Features: - Time-series database - Powerful query language (PromQL) - Service discovery - Alerting rules

Configuration:

# Prometheus configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: monitoring
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s

    scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod
        relabel_configs:
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
            action: keep
            regex: true

Grafana¶

Purpose: Data visualization and dashboard creation

Features: - Rich dashboard creation - Multiple data source support - Alerting and notifications - User management

Dashboards: - Cluster Overview: Node status, resource usage - Application Metrics: Response times, error rates - Infrastructure Health: System performance - Chaos Engineering: Experiment results

AlertManager¶

Purpose: Alert routing and notification management

Features: - Alert deduplication - Grouping and routing - Multiple notification channels - Silence management

Configuration:

# AlertManager configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: alertmanager-config
  namespace: monitoring
data:
  alertmanager.yml: |
    global:
      slack_api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'

    route:
      group_by: ['alertname']
      group_wait: 10s
      group_interval: 10s
      repeat_interval: 1h
      receiver: 'slack-notifications'

    receivers:
    - name: 'slack-notifications'
      slack_configs:
      - channel: '#alerts'
        title: '{{ template "slack.title" . }}'
        text: '{{ template "slack.text" . }}'

3. Self-Healing Components¶

Self-Healing Controller¶

Purpose: Automated failure detection and recovery

Core Logic:

class SelfHealingController:
    def __init__(self):
        self.health_check_interval = 30
        self.node_failure_threshold = 3
        self.pod_restart_threshold = 5
        self.slack_webhook = os.getenv('SLACK_WEBHOOK_URL')

    def monitor_cluster(self):
        """Monitor cluster health and trigger recovery actions"""
        while True:
            try:
                # Check node health
                self.check_node_health()

                # Check pod health
                self.check_pod_health()

                # Check service health
                self.check_service_health()

                time.sleep(self.health_check_interval)
            except Exception as e:
                self.send_alert(f"Monitoring error: {e}")

    def check_node_health(self):
        """Check node status and trigger recovery if needed"""
        nodes = self.k8s_client.list_node()
        for node in nodes.items:
            if node.status.conditions:
                for condition in node.status.conditions:
                    if condition.type == "Ready" and condition.status == "False":
                        self.handle_node_failure(node)

    def handle_node_failure(self, node):
        """Handle node failure by cordoning and draining"""
        try:
            # Cordon the node
            self.k8s_client.patch_node(
                name=node.metadata.name,
                body={"spec": {"unschedulable": True}}
            )

            # Drain the node
            self.drain_node(node.metadata.name)

            # Send notification
            self.send_alert(f"Node {node.metadata.name} has been cordoned and drained")

        except Exception as e:
            self.send_alert(f"Failed to handle node failure: {e}")

Health Check Service¶

Purpose: Comprehensive health monitoring

Health Checks: - Liveness Probes: Application responsiveness - Readiness Probes: Service availability - Startup Probes: Application startup status

Configuration:

# Health check configuration
apiVersion: apps/v1
kind: Deployment
metadata:
  name: health-check-service
spec:
  template:
    spec:
      containers:
      - name: app
        image: my-app:latest
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
        startupProbe:
          httpGet:
            path: /startup
            port: 8080
          failureThreshold: 30
          periodSeconds: 10

4. Chaos Engineering Components¶

Chaos Mesh¶

Purpose: Chaos engineering platform for failure injection

Experiment Types: - Pod Chaos: Pod failure, pod kill - Network Chaos: Network delay, network loss - IO Chaos: IO delay, IO error - Kernel Chaos: Kernel panic, memory corruption

Configuration:

# Chaos experiment configuration
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
  name: pod-failure-experiment
spec:
  action: pod-failure
  mode: one
  selector:
    namespaces: [default]
    labelSelectors:
      app: test-app
  duration: 30s
  scheduler:
    cron: "@every 10m"

Chaos Dashboard¶

Purpose: Visualize and manage chaos experiments

Features: - Experiment scheduling - Real-time monitoring - Result analysis - Experiment history

5. CI/CD Components¶

GitHub Actions¶

Purpose: Automated build, test, and deployment pipeline

Workflow Stages: 1. Build: Compile and package applications 2. Test: Run unit and integration tests 3. Security Scan: Vulnerability scanning 4. Deploy: Automated deployment to environments

Configuration:

# GitHub Actions workflow
name: CI/CD Pipeline
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - name: Build application
      run: |
        docker build -t my-app:${{ github.sha }} .
        docker push my-app:${{ github.sha }}

  test:
    runs-on: ubuntu-latest
    needs: build
    steps:
    - name: Run tests
      run: |
        kubectl apply -f kubernetes/test-app/
        kubectl wait --for=condition=ready pod -l app=test-app
        kubectl exec test-app -- npm test

  deploy:
    runs-on: ubuntu-latest
    needs: [build, test]
    steps:
    - name: Deploy to production
      run: |
        kubectl set image deployment/my-app my-app=my-app:${{ github.sha }}
        kubectl rollout status deployment/my-app

6. Security Components¶

RBAC (Role-Based Access Control)¶

Purpose: Fine-grained access control

Configuration:

# RBAC configuration
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: self-healing-role
rules:
- apiGroups: [""]
  resources: ["pods", "nodes", "services"]
  verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["apps"]
  resources: ["deployments", "replicasets"]
  verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]

Network Policies¶

Purpose: Micro-segmentation and network security

Configuration:

# Network policy
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny
  namespace: default
spec:
  pod_selector: {}
  policy_types:
  - Ingress
  - Egress

Secrets Management¶

Purpose: Secure credential and secret storage

Configuration:

# Secret configuration
apiVersion: v1
kind: Secret
metadata:
  name: app-secrets
type: Opaque
data:
  database-url: <base64-encoded-url>
  api-key: <base64-encoded-key>

Component Interactions¶

Data Flow¶

1. Monitoring: Prometheus collects metrics from all components
2. Analysis: Self-healing controller analyzes metrics for anomalies
3. Detection: Failures are detected through health checks and metrics
4. Recovery: Automated recovery actions are triggered
5. Notification: Alerts are sent via Slack/email
6. Documentation: All actions are logged for audit

Event Handling¶

• Node Failure: Cordon, drain, and replace node
• Pod Crash: Restart pod or scale deployment
• Service Unavailable: Restart service or failover
• Resource Exhaustion: Scale resources or migrate workloads

Performance Characteristics¶

Scalability¶

• Horizontal Scaling: Auto-scaling based on metrics
• Vertical Scaling: Resource limits and requests
• Load Balancing: Service mesh for traffic distribution

Reliability¶

• High Availability: Multi-zone deployment
• Fault Tolerance: Redundant components
• Disaster Recovery: Backup and restore procedures

Monitoring¶

• Real-time Metrics: Sub-second metric collection
• Alerting: Proactive issue detection
• Logging: Comprehensive audit trails