Enabling Autonomic Microservice Management through Self-Learning Agents

Fenglin Yu, Fangkai Yang, Xiaoting Qin, Zhiyang Zhang, Jue Zhang, Qingwei Lin, Hongyu Zhang, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

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.