title: The Vision of Autonomic Computing: Can LLMs Make It a Reality?
summary: 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.
paper_authors._id: 669dbfaffe9496b3c6e5b1d3
paper_authors.name: Zhiyang Zhang
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paper_authors.user: beizhi0109
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paper_authors.name: Fangkai Yang
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paper_authors.fullname: kaikai yang
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paper_authors.user: keanudicap
paper_authors._id: 669dbfaffe9496b3c6e5b1d5
paper_authors.name: Xiaoting Qin
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paper_authors.fullname: Xiaoting Qin
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paper_authors._id: 669dbfaffe9496b3c6e5b1d6
paper_authors.name: Jue Zhang
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paper_authors.fullname: Jue Zhang
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paper_authors.user: JueZhang
paper_authors._id: 669dbfaffe9496b3c6e5b1d7
paper_authors.name: Qingwei Lin
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paper_authors.fullname: Lin Qingwei
paper_authors.type: user
paper_authors.user: Eliblo1969
paper_authors._id: 669dbfaffe9496b3c6e5b1d8
paper_authors.name: Gong Cheng
paper_authors._id: 669dbfaffe9496b3c6e5b1d9
paper_authors.name: Dongmei Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T08:49:08.510Z
paper_authors._id: 66473d2c7abe6ad66e81a3dd
paper_authors.avatarUrl: /avatars/82f40244806c06ffeaa1c4265e9725ea.svg
paper_authors.fullname: ZHANGDONGMEI
paper_authors.type: user
paper_authors.user: ZDM6426
paper_authors._id: 669dbfaffe9496b3c6e5b1da
paper_authors.name: Saravan Rajmohan
paper_authors._id: 669dbfaffe9496b3c6e5b1db
paper_authors.name: Qi Zhang
paper_title: The Vision of Autonomic Computing: Can LLMs Make It a Reality?
paper_summary: 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.
paper_ai_summary: An LLM-based multi-agent framework enhances microservice management towards achieving autonomic computing, demonstrating progress in Autonomous Service Maintenance.
paper_ai_keywords: Autonomic Computing Large Language Models multi-agent framework microservice management autonomous service maintenance