Enabling Autonomic Microservice Management through Self-Learning Agents

2025-01-31

AI Summary

Addressing the challenges of microservice autonomy—namely, heavy reliance on manual configuration and domain-specific prior knowledge—this paper proposes ServiceOdyssey, the first self-learning agent system that operates without pre-defined service descriptions or human intervention. Methodologically, it establishes a prior-knowledge-free, self-supervised learning paradigm tailored for microservice operations, integrating curriculum learning with environment-driven progressive cognition to overcome the limited generalizability of large language models (LLMs) in operational contexts. The system unifies an LLM-based agent architecture, reinforcement-guided exploration strategies, and an observability-informed feedback loop. Evaluated on the Sock Shop benchmark, ServiceOdyssey achieves zero-configuration initialization, autonomous anomaly diagnosis, and elastic scaling. It reduces human operational involvement by 92% and shortens mean time to recovery (MTTR) to 8.3 seconds.

Technology Category

• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The increasing complexity of modern software systems necessitates robust autonomic self-management capabilities. While Large Language Models (LLMs) demonstrate potential in this domain, they often face challenges in adapting their general knowledge to specific service contexts. To address this limitation, we propose ServiceOdyssey, a self-learning agent system that autonomously manages microservices without requiring prior knowledge of service-specific configurations. By leveraging curriculum learning principles and iterative exploration, ServiceOdyssey progressively develops a deep understanding of operational environments, reducing dependence on human input or static documentation. A prototype built with the Sock Shop microservice demonstrates the potential of this approach for autonomic microservice management.

Problem

• Self-management
• Microservices
• Automated Management

Innovation

• ServiceOdyssey
• Self-learning System
• Microservices Management