LEMON: LLM-Enabled Monitoring for Microservices Orchestration

The complexity of modern microservice architectures has surpassed the capabilities of traditional orchestration tools, which rely on static, manual workflows. This limits scalability and adaptability, especially to dynamic workloads. This paper argues for a paradigm shift towards self-managing, intent-driven microservices orchestration systems, where human operators express high-level goals in natural language. As a foundational step towards this vision of autonomous orchestration agents, we introduce LEMON: a new architecture that leverages Large Language Models (LLMs) for intelligent microservice monitoring. We also propose a comprehensive classification to structure this emerging field. Our evaluation demonstrates that fine-tuning Small Language Models (SLMs) for intent classification significantly enhances accuracy while ensuring the model's output is reliably structured for automation. Furthermore, our analysis of the trade-offs between model size, precision, and latency provides a practical guide for deploying these systems. We foresee this monitoring capability as the critical ”sense” component toward autonomous microservices orchestration loops. By diagnosing performance bottlenecks from natural language queries, LEMON enables future systems to automatically suggest and execute solutions. This work lays the groundwork for truly self-managing, intent-driven systems.