---

What is Auto Remediation?

Auto Remediation refers to the automatic detection and resolution of incidents in IT systems without manual intervention. It bridges the gap between monitoring, alerting, and action by using scripts, tools, and services to recover from failures in real-time.

Purpose:

• Reduce mean time to recovery (MTTR)
• Minimize human errors
• Enhance system resilience and availability

Key Concepts:

• Self-healing systems
• Automated diagnostics
• Trigger-based response actions

---

Why Auto Remediation is Important

• Speed: Automated recovery happens faster than human intervention.
• Scalability: Supports growing infrastructure without scaling human effort.
• Consistency: Reduces variance in responses to issues.
• Availability: Increases uptime by preventing cascading failures.

Note: According to Google’s SRE book, eliminating toil through automation is one of the main tenets of reliability engineering.

---

Real-World Examples

1. AWS Lambda auto-restarts EC2 if CPU usage is 100% for 5 minutes.

2. Kubernetes automatically restarts a pod if it crashes.

3. Azure Automation shuts down unused VMs after hours.

4. Ansible script resolves disk space issues by cleaning logs.

---

Monitoring and Incident Detection

Prometheus Example:

group: instance-health
rules:
  - alert: HighCPUUsage
    expr: avg(rate(node_cpu_seconds_total{mode="user"}[5m])) by (instance) > 0.8
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "High CPU usage on {{ $labels.instance }}"
Code language: JavaScript (javascript)

AWS CloudWatch Example:

• Set metric alarms on EC2
• Trigger SNS topic
• SNS calls Lambda function

---

Triggering Automated Responses

AWS Lambda Example:

import boto3

def lambda_handler(event, context):
    ec2 = boto3.client('ec2')
    ec2.reboot_instances(InstanceIds=['i-0abcd1234'])
Code language: JavaScript (javascript)

Azure Automation:

• Use PowerShell runbooks

Ansible + Rundeck:

ansible-playbook restart-service.yml
Code language: CSS (css)

StackStorm:

---
trigger:
  type: core.st2.webhook
  parameters:
    url: /alert
action:
  ref: my_pack.restart_service

---

Incident Management System Integration

Opsgenie:

• Auto-acknowledge alerts
• Execute scripts or trigger workflows

ServiceNow:

• Auto-create/change incidents based on events
• Trigger remediation flows via Flow Designer

---

Runbooks and Playbooks

Sample Playbook:

- name: Disk Cleanup
  hosts: all
  tasks:
    - name: Clean up old logs
      file:
        path: "/var/log/app/*.log"
        state: absent
Code language: JavaScript (javascript)

Runbook Steps:

1. Identify log directory
2. Clean if disk > 90%
3. Notify Slack channel

---

Auto Healing Kubernetes Pods

Liveness & Readiness Probes:

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

Horizontal Pod Autoscaler (HPA):

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
spec:
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

---

Auto Remediation with Terraform, Python, and Shell

Terraform + Lambda:

resource "aws_lambda_function" "reboot_ec2" {
  filename      = "reboot.zip"
  handler       = "reboot.lambda_handler"
  runtime       = "python3.9"
  role          = aws_iam_role.lambda_exec.arn
}
Code language: JavaScript (javascript)

Shell Script Example:

#!/bin/bash
if [ $(df / | awk 'NR==2 {print $5}' | sed 's/%//') -gt 90 ]; then
  rm -rf /var/log/*
fi
Code language: JavaScript (javascript)

---

Real-World Architecture & Use Cases

Example: EC2 Auto Remediation

[CloudWatch] → [SNS] → [Lambda] → [EC2 Action]
Code language: CSS (css)

Example: Kubernetes Crash Recovery

[Prometheus] → [Alertmanager] → [Webhook] → [Script]
Code language: CSS (css)

Use Cases:

• App crash recovery
• DB failover
• Disk cleanup
• Restart services

---

Pros, Cons, and Risks

✅ Pros:

• Fast recovery
• 24/7 coverage
• Less toil

❌ Cons:

• Over-remediation
• False positives

⚠️ Risks:

• Remediation loops
• Dependency conflicts
• Security implications

---

Best Practices

• Always start with manual playbooks before automation.
• Implement rate limiting or cooldown periods.
• Use logging and observability.
• Add audit logs to track automatic actions.
• Simulate failures in test environments before production.

---

Sample Projects

• GitHub: Auto EC2 Remediation
• GitHub: Kubernetes Auto-Heal

---

Glossary

• MTTR: Mean Time to Recovery
• Runbook: A manual guide for resolving incidents
• Playbook: Automated form of a runbook
• Remediation Loop: Continuous triggering of remediation without resolution
• Liveness Probe: Health check for containers

---

FAQ

Q1: What if the script fails to remediate?

Add fallback or notify human on failure.

Q2: Can we disable auto-remediation?

Yes, use flags or feature toggles.

Q3: Is AI used in remediation?

Yes, in advanced systems like AIOps.

---

Quizzes

1. What does MTTR stand for?

• Mean Time to Recovery
• Manual Testing Through Regression

2. Which Kubernetes feature allows pod recovery?

• ConfigMap
• Liveness Probe

3. Which tool is NOT used for auto remediation?

• StackStorm
• Excel

---

End of Tutorial – Happy Automating!