Kubernetes with Rust, MicroK8s, cert-manager, Gateway API, K8sGPT, and LocalAI

This repository demonstrates AI-driven Kubernetes clusters managed with MicroK8s, integrating a Rust application that interacts with the Kubernetes API using Tokio and the kube crate. It features modern service networking with the Gateway API, TLS certificate management via cert-manager, and AI capabilities using K8sGPT for cluster analytics and LocalAI for API-compatible AI inference endpoints.

🚧 🛠️ EXPERIMENTAL STATUS AND DEVELOPMENT PAUSE

Before reading further, please note that this project serves entirely as an experimental and educational experience for learning Kubernetes and Rust. It was developed on a local Ubuntu operating system without containerization. The development clusters can be challenging to replicate locally due to non-public environment configurations; therefore, instructions are not provided. The codebase has reached a state where new feature development has been paused entirely until a CI testing workflow is implemented, as documented in #48.

⚠️ Development-Only Notice

The local Kubernetes deployments use TLS certificates created by a selfsigned-issuer, which are suitable for experimental development only and NOT intended for production use cases. A transition to a production-grade setup would require using Let's Encrypt or another trusted certificate authority.

Features

Gateway API: Next-generation service networking with HTTPRoute for granular traffic control and TLS.
Cert-Manager: Automates TLS certificate issuance using a selfsigned-issuer for development environments.
Rust Application: A minimal implementation demonstrating Kubernetes API interaction by listing Pods across all namespaces with their statuses, serving as a foundation for building advanced cluster operations such as pod management, resource monitoring, CRD handling, event watching, and multi-cluster management.
K8sGPT: Cluster diagnostics powered by AI.
LocalAI: Self-hosted AI capabilities with persistent storage for models, offering potential for privacy-focused, scalable, and cost-efficient AI inference endpoint deployment.

Scalability Benefits for AI Workloads

Horizontal Pod Autoscaling: Automatically scale LLM inference endpoints based on demand, ensuring optimal resource utilization and consistent response times even under varying loads. Multiple LocalAI instances can be deployed across nodes to handle increased traffic without service degradation.
Resource Optimization: Leverage Kubernetes' native scheduling and resource management to efficiently distribute AI workloads across the cluster, enabling dynamic allocation of GPU and CPU resources based on model requirements and usage patterns.

Potential Direction

Production-Grade TLS Certificates: Transition to production-ready deployments by integrating trusted certificate authorities like Let's Encrypt or custom enterprise CAs for secure and scalable HTTPS traffic management.
Dynamic Cluster Management: Automate scaling, monitoring, and resource optimization across multiple clusters.
Custom Resource Definitions (CRDs): Implement and manage custom Kubernetes resources tailored to specific application requirements.
Event-Driven Automation: Extend the Rust app to respond to Kubernetes events or webhooks for real-time cluster adjustments.
AI Workload Orchestration: Use LocalAI to manage and deploy advanced AI inference systems for edge computing, predictive analytics, or machine learning tasks.
Security Enhancements: Integrate advanced authentication mechanisms and Role-Based Access Control (RBAC) policies for secure multi-user environments.
Multi-Tenancy Support: Enable resource isolation and quota management for multi-tenant Kubernetes clusters.
Advanced Networking: Leverage Gateway API features for traffic splitting, failover mechanisms, and routing policies based on performance metrics.
Hybrid Cloud Deployments: Adapt the setup for hybrid or multi-cloud Kubernetes deployments.
Natural Language Processing (NLP): Implement AI-powered features such as text summarization, sentiment analysis, or chatbot functionality for applications requiring language understanding.
Image and Video Processing: Use AI models to enable facial recognition, object detection, image classification, or video analytics for multimedia applications.
Real-Time Data Stream Processing: Integrate AI models to process and analyze high-velocity data streams (e.g., IoT sensor data, live event tracking, or financial market feeds) for real-time insights and predictions.
AI-Powered Infrastructure Management: Automate cluster health monitoring and resource allocation using predictive analytics to identify performance bottlenecks and self-heal infrastructure issues before they escalate.
Scientific Simulations and Modeling: Use AI to accelerate complex scientific simulations, such as climate modeling, molecular dynamics, or astrophysical computations, leveraging Kubernetes' scalable GPU resources.
Context-Aware API Gateways: Use AI models on Kubernetes endpoints to dynamically analyze incoming API requests and provide context-aware routing, such as adjusting traffic flow based on user behavior, request intent, or predicted resource demands. This can enhance scalability and improve user experience by intelligently prioritizing requests.
Personalized Response Generation: Deploy AI models on endpoints to deliver tailored responses to users, such as real-time content recommendations, adaptive UI/UX experiences, or personalized chatbot interactions. By integrating AI with Kubernetes, these models can scale based on traffic while ensuring low-latency, user-specific outputs for high-demand applications.
Predictive Autoscaling for Endpoint Workloads: Use AI inference endpoints on Kubernetes to predict traffic patterns and proactively scale resources. By analyzing historical and real-time data, the AI can optimize pod scaling to handle peak loads efficiently, reducing latency and preventing over-provisioning while ensuring seamless endpoint performance.