自主计算的愿景：LLMs能否使其成为现实？

The Vision of Autonomic Computing: Can LLMs Make It a Reality?

July 19, 2024

作者: Zhiyang Zhang, Fangkai Yang, Xiaoting Qin, Jue Zhang, Qingwei Lin, Gong Cheng, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

cs.AI

摘要

提出了两个多年前的自主计算（ACV）愿景，设想计算系统能够像生物体一样自我管理，无缝适应不断变化的环境。尽管经过数十年的研究，由于现代计算系统的动态和复杂性，实现ACV仍然具有挑战性。最近大型语言模型（LLMs）的进展为解决这些挑战提供了希望，通过利用它们丰富的知识、语言理解和任务自动化能力。本文通过基于LLM的多智能体框架探讨了实现ACV的可行性，用于微服务管理。我们引入了一个五级分类法，用于自主服务维护，并基于Sock Shop微服务演示项目提出了一个在线评估基准，以评估我们框架的性能。我们的研究结果显示了朝着实现第三级自主性的重大进展，突显了LLMs在检测和解决微服务架构中问题方面的有效性。这项研究通过在微服务管理框架中首创将LLMs整合，为推动自主计算做出了贡献，为更具适应性和自我管理的计算系统铺平了道路。代码将在https://aka.ms/ACV-LLM 上提供。

English

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.