[AI Powered Kubernetes Autoscaler](https://medium.com/@shivangx27/ai-powered-kubernetes-autoscaler-1590a5207b4e)

![[Pasted image 20250522223924.png]]


## ✅ Functional Requirements

| Requirement Category       | Description                                                             |
| -------------------------- | ----------------------------------------------------------------------- |
| Container Monitoring       | Monitor CPU, memory, network I/O, and logs; expose `/metrics` endpoint. |
| Anomaly Detection          | Train LSTM on metrics; score real-time data for anomalies.              |
| Knowledge Graph + RAG      | Retrieve context (logs, playbooks, docs) via vector search for LLM.     |
| RCA & Remediation          | Correlate data, suggest/execute fixes, validate via OPA/Gatekeeper.     |
| Summarization & Reporting  | Generate incident summaries, post-mortems, daily digests.               |
| Natural Language Interface | Answer queries like "Why did CPU spike at 4 PM?" using LLM + context.   |
| HPA Metric Exposure        | Expose custom metrics (`graphrag_anomaly_score`) for HPA use.           |

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## ✅ Non-Functional Requirements

| Requirement Type | Description                                                         |
| ---------------- | ------------------------------------------------------------------- |
| Scalability      | Works per-pod via sidecar, scales with app count.                   |
| Modularity       | LSTM, GraphRAG, and app core are independently deployable.          |
| Low Latency      | Inference and actions respond in 1–3 seconds.                       |
| Security         | RBAC for K8s actions; secure sidecar communication; OPA validation. |
| Resilience       | Sidecar failures don’t affect app; fallback LLM response available. |

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## ✅ System Requirements

| Component        | Requirement                                                              |
| ---------------- | ------------------------------------------------------------------------ |
| Hardware         | GPU optional; moderate RAM/CPU; supports container-level processing.     |
| Software Stack   | Kubernetes, Prometheus, Grafana, Loki/EFK, Neo4j, FAISS/Chroma, OPA.     |
| Language/Runtime | Python (ML), Go/Rust (agents), LLM (HuggingFace/vLLM), Kubectl bindings. |
| Dependencies     | MLflow, Vector DB, Graph DB, OPA, OpenTelemetry.                         |

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## ✅ Tooling & DevOps Requirements

| Area            | Description                                                              |
| --------------- | ------------------------------------------------------------------------ |
| CI/CD           | Docker builds, Helm deployment, GitOps (ArgoCD/Flux).                    |
| Model Lifecycle | MLflow/S3 for versioning, optional SageMaker/Kubeflow.                   |
| Observability   | Grafana dashboards, custom anomaly views, OpenTelemetry support.         |
| Testing         | Unit + integration + chaos testing across ML and remediation components. |
