The Vision of Autonomic Computing: Can LLMs Make It a Reality?
Authors: name: Zhiyang Zhang, name: Fangkai Yang, name: Xiaoting Qin, name: Jue Zhang, name: Qingwei Lin, name: Gong Cheng, name: Dongmei Zhang, name: Saravan Rajmohan, name: Qi Zhang

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.