自主計算的願景：LLMs 能讓它變成現實嗎？

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

提出二十多年前的自主計算（ACV）願景，設想計算系統能夠像生物有機體一樣自我管理，無縫適應不斷變化的環境。儘管經過數十年的研究，實現ACV仍然具有挑戰性，這是由於現代計算系統的動態和複雜性質。最近大規模語言模型（LLMs）的進步為應對這些挑戰提供了有希望的解決方案，通過利用它們廣泛的知識、語言理解和任務自動化能力。本文通過基於LLM的多代理框架來探討實現ACV的可行性，用於微服務管理。我們提出了一個五級分類法，用於自主服務維護，並提出了一個基於Sock Shop微服務演示項目的在線評估基準，以評估我們框架的性能。我們的研究結果顯示在實現第3級自主性方面取得了顯著進展，突出了LLMs在檢測和解決微服務架構中問題方面的有效性。本研究通過將LLMs整合到微服務管理框架中，推動了自主計算的進步，為更具適應性和自我管理的計算系統鋪平了道路。代碼將在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.