Problem
Research questions and friction points this paper is trying to address.
Microservices
Compute Continuum
Service Level Objectives
Intent-Based Placement
MicroIntent: Intent-Based Placement Strategy for Microservice Application in the Compute Continuum Using LLMs
🤖 AI Summary
本文提出了一种基于自然语言描述的用户意图，利用生成式人工智能将意图转化为低级SLOs，并据此在计算连续体中进行微服务放置的方法。
📝 Abstract
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 highlevel 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative Artificial Intelligence
High-level User Intents
Service Level Objectives (SLOs)
Microservices Placement
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