Design an AI-Assisted Monitoring and Auto-Remediation Service

Context

Design a service that monitors cloud applications across multiple providers, collects telemetry (metrics, logs, traces, events), invokes an AI-based analyzer to detect incidents, and automatically takes actions such as shutting down or network-isolating instances. The system must work at scale and be resilient to noisy alerts.

Requirements

Functional

1. Data ingestion
   • Support metrics, logs, traces, events; streaming and near real-time.
   • Schema/versioning, tenant isolation, and backpressure handling.
2. Model serving
   • Real-time scoring; model registry/versioning; feature store.
3. Rule and AI fusion
   • Combine deterministic rules with ML outputs to decide severity and actions.
4. Action orchestration
   • Execute runbooks: e.g., instance shutdown, quarantine via network policies, restart, scale-out.
   • Idempotency, retries, connectors to major cloud providers.
5. Safety checks
   • Human-in-the-loop where needed, blast-radius limits, budgets, kill switches, canaries.
6. Audit logs
   • Append-only, tamper-evident logging of telemetry-derived incidents, decisions, and actions.
7. Rollback
   • Automatic or manual rollback with state capture and time-bound isolation.

Non-Functional

• Multi-cloud support (e.g., AWS, Azure, GCP; on-prem optional).
• Scale and performance (define SLOs/latency, horizontal scaling, capacity planning).
• Noisy alert reduction (deduplication, rate limiting, correlation, adaptive thresholds).

Deliverables

• Architecture with key components and data flow.
• Choices/trade-offs for ingestion, storage, model serving, fusion, orchestration.
• Safety and governance mechanisms.
• Plan for multi-cloud integration, scaling, and noisy alert handling.