自学习智能体让微服务自动管理，无需预先配置
Enabling Autonomic Microservice Management through Self-Learning Agents
- 用课程学习+迭代探索构建自适应管理能力
- 原型测试显示可减少对人工干预的依赖
- 适合运维自动化与云原生系统开发者
现代软件系统日益复杂，亟需强大的自主管理能力。尽管大语言模型（LLMs）在该领域展现潜力，但往往难以将通用知识适配到具体服务场景。为此，我们提出 ServiceOdyssey——一个无需预先掌握服务配置即可自主管理微服务的自学习智能体系统。通过借鉴课程学习原理与迭代探索机制，ServiceOdyssey逐步深化对运行环境的理解，降低对人工输入或静态文档的依赖。基于 Sock Shop 微服务构建的原型验证了该方法在自主微服务管理中的可行性。
原文摘要 · Abstract (English)
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.
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