The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing environments. Despite decades of research, achieving ACV remains challenging due to the dynamic and complex nature of modern computing systems. Recent advancements in Large Language Models (LLMs) offer promising solutions to these challenges by leveraging their extensive knowledge, language understanding, and task automation capabilities. This paper explores the feasibility of realizing ACV through an LLM-based multi-agent framework for microservice management. We introduce a five-level taxonomy for autonomous service maintenance and present an online evaluation benchmark based on the Sock Shop microservice demo project to assess our framework's performance. Our findings demonstrate significant progress towards achieving Level 3 autonomy, highlighting the effectiveness of LLMs in detecting and resolving issues within microservice architectures. This study contributes to advancing autonomic computing by pioneering the integration of LLMs into microservice management frameworks, paving the way for more adaptive and self-managing computing systems. The code will be made available at https://aka.ms/ACV-LLM.

翻译：自主计算愿景（ACV）于二十多年前提出，其设想是计算系统能够像生物有机体一样自我管理，无缝适应不断变化的环境。尽管历经数十年的研究，由于现代计算系统的动态性和复杂性，实现ACV仍然面临挑战。大型语言模型（LLMs）的最新进展，通过利用其广泛的知识、语言理解能力和任务自动化能力，为应对这些挑战提供了有前景的解决方案。本文探讨了通过一个基于LLM的多智能体框架实现微服务管理以达成ACV的可行性。我们引入了一个用于自主服务维护的五级分类法，并提出了一个基于Sock Shop微服务演示项目的在线评估基准，以评估我们框架的性能。我们的研究结果表明，在实现第三级自主性方面取得了显著进展，突显了LLMs在检测和解决微服务架构问题方面的有效性。本研究通过率先将LLMs集成到微服务管理框架中，为推进自主计算做出了贡献，为更具适应性和自我管理能力的计算系统铺平了道路。代码将在 https://aka.ms/ACV-LLM 公开。