AutoOps — Self-Healing Kubernetes Platform

A cloud-native DevOps platform that simulates production-grade deployment automation, observability, and self-healing workflows on a local Kubernetes cluster.

The project demonstrates:

• GitOps-based deployments using ArgoCD
• Kubernetes orchestration with k3d/kind
• Automated monitoring with Prometheus + Grafana
• AI-assisted anomaly detection workflows
• CI/CD automation using GitHub Actions
• Infrastructure provisioning using Terraform

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Architecture Overview

Core Workflow

1. Developers push application changes to GitHub.
2. GitHub Actions runs CI pipelines and validates builds.
3. ArgoCD synchronizes Kubernetes manifests into the cluster.
4. Prometheus collects metrics and Loki aggregates logs.
5. AI monitoring services analyze telemetry data for anomalies.
6. Self-healing workflows trigger restart, rollback, or scaling actions.

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Technology Stack

Layer	Tools
Container Orchestration	Kubernetes (k3d/kind)
GitOps Deployment	ArgoCD, Helm
CI/CD	GitHub Actions
Monitoring	Prometheus, Grafana
Logging	Loki
Tracing	OpenTelemetry, Jaeger
Infrastructure as Code	Terraform
AI Monitoring	Python, FastAPI
Containerization	Docker

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Key Features

GitOps Deployment Pipeline

• Automated deployments using ArgoCD
• Helm-based Kubernetes application management
• Git-driven infrastructure synchronization

Observability Stack

• Real-time metrics collection with Prometheus
• Grafana dashboards for latency, throughput, and error monitoring
• Centralized log aggregation using Loki
• Distributed tracing with OpenTelemetry + Jaeger

AI-Assisted Monitoring

• Detects anomalous infrastructure behavior using ML-based analysis
• Simulates predictive monitoring workflows for Kubernetes workloads
• Supports automated remediation triggers

Self-Healing Automation

• Automatic pod restart workflows
• Rollback simulation for failed deployments
• Alert-driven remediation logic using Prometheus alerts

Secure CI/CD

• Container vulnerability scanning
• Automated test execution
• GitHub Actions-based deployment workflows

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Project Structure

AutoPilotOps/
├── .github/workflows/      # CI/CD pipelines
├── terraform/              # Infrastructure definitions
├── k8s/
│   ├── manifests/          # Kubernetes YAML manifests
│   └── helm-charts/        # Helm charts
├── monitoring/
│   ├── prometheus/
│   ├── grafana/
│   └── loki/
├── ai-monitoring/
│   ├── model/
│   └── service/
├── operator/               # Self-healing controller logic
├── scripts/                # Utility and failure simulation scripts
└── docs/                   # Architecture and design docs

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Local Setup

Prerequisites

Install:

• Docker
• kubectl
• k3d or kind
• Helm
• Terraform
• Python 3.11+
• GitHub CLI (optional)

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Run Locally

# Create cluster
k3d cluster create autopilotops

# Install ArgoCD
kubectl create namespace argocd
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

# Deploy monitoring stack
helm install prometheus prometheus-community/kube-prometheus-stack

# Run services
kubectl apply -f k8s/manifests/

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Future Improvements

• AI-driven autoscaling policies
• Drift detection for Kubernetes resources
• Multi-cluster GitOps deployment
• Slack/Discord alert integration
• Chaos engineering simulations

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Learning Outcomes

This project demonstrates practical experience with:

• Kubernetes operations
• GitOps workflows
• Observability engineering
• Infrastructure automation
• Production-style CI/CD
• AI-assisted infrastructure monitoring

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Disclaimer

This project is designed as a local production simulation environment for DevOps, observability, and cloud-native automation patterns.