发表机构
Western Sydney University; Federal Institute of Education, Science and Technology Sul-Riograndense (IFSUL)（西悉尼大学; 南里奥格兰德联邦教育、科学和技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MicroIntent架构，利用大语言模型将用户自然语言意图转化为低级别SLO，以指导计算连续体中的微服务放置，并通过原型验证其可行性。
AI 中文摘要
微服务在计算连续体中的放置对于提供满足客户需求的服务（如降低延迟、存储需求、服务质量和可用性）起着至关重要的作用。为了在计算连续体的地理分散架构中实现客户需求，服务级别目标（SLO）已被广泛用于微服务放置的决策中。然而，由于低级别的SLO增加了连续体用户的使用门槛，若要大规模采用计算连续体，则需要基于高级业务词汇的放置决策。本文提出了一种利用自然语言描述的高级用户意图作为输入，在计算连续体中做出微服务放置决策的架构。该方法利用生成式人工智能将意图转换为低级别的SLO，这些SLO与基础设施描述一起用于决定组成应用的各个微服务应部署在何处，以满足SLO。我们实现并评估了该架构的原型，以证明该方法的可行性。
英文摘要
The placement of microservices in the compute continuum plays a vital role in delivering services that comply with customers' needs, such as reduced latency, storage requirements, quality of service and availability. To achieve customers' needs in the geographically dispersed architecture of the compute continuum, Service Level Objectives (SLOs) have been largely used in decision-making to place microservices. However, because low-level SLOs increase the barrier to entry for continuum users, placement decisions based on high-level business vocabulary are required if the compute continuum is to be adopted at scale. This paper proposes an architecture for microservices placement decisions in the computing continuum utilizing high-level user intents described in natural language as input. The approach utilizes Generative Artificial Intelligence to translate the intents to low-level SLOs, which are used along with the infrastructure description to decide where different microservices that compose an application must be deployed so that SLOs are met. We implement and evaluate a prototype of the architecture to demonstrate the approach's feasibility.
Journal refProceedings of the 2025 IEEE International Conference on Smart Internet of Things (SmartIoT 2025), pp. 124-131, 2025
DOI：10.1109/SmartIoT66867.2025.00026
