ChatOps for microservice systems: A low-code approach using service composition and large language models

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-07-18 DOI:10.1016/j.future.2024.07.029

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Abstract

The Microservice Architecture (MSA) plays a pivotal role in contemporary e-business, promoting service independence, autonomy, and continual evolution in line with the principles of DevOps. However, the distributed nature of the MSA introduces additional complexity, which requires familiarity with multiple DevOps (Development and Operations) tools, thereby increasing the learning curve. This paper presents a specialized ChatOps (Chat Operations) approach that allows MSA developers to compose new ChatOps capabilities in a low-code way (i.e., with minimal coding). The proposed ChatOps4Msa approach leverages established ChatOps functionalities to facilitate the real-time monitoring of service status, conduct service testing, track issues, and receive alerts using natural language or the proposed ChatOps Query Language (CQL). The use of large language models (LLMs) for functional intents also enhances the usability of the DevOps toolchain in microservices systems to streamline implementation.

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微服务系统的 ChatOps：使用服务组合和大型语言模型的低代码方法

微服务架构（MSA）在当代电子商务中发挥着举足轻重的作用，它促进了服务的独立性、自主性以及与 DevOps 原则相一致的持续演进。然而，MSA 的分布式特性带来了额外的复杂性，需要熟悉多种 DevOps（开发和运营）工具，从而增加了学习曲线。本文提出了一种专门的 ChatOps（聊天运营）方法，允许 MSA 开发人员以低码方式（即只需最少的编码）组成新的 ChatOps 功能。所提出的 ChatOps4Msa 方法利用已建立的 ChatOps 功能来促进对服务状态的实时监控、进行服务测试、跟踪问题以及使用自然语言或所提出的 ChatOps 查询语言（CQL）接收警报。针对功能意图使用大型语言模型（LLM）还能提高 DevOps 工具链在微服务系统中的可用性，从而简化实施过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.