Microservice placement optimized using AI to meet user intent in compute continuum
MicroIntent: Intent-Based Placement Strategy for Microservice Application in the Compute Continuum Using LLMs
Distributed, Parallel, and Cluster Computing
Summary
Placing small pieces of software called microservices in a network that spans many locations can be tricky because users want certain things like fast response and reliability. The authors propose a system that lets users describe what they want in everyday language. Then, an artificial intelligence tool translates these user wishes into technical requirements and decides where each microservice should run. Their prototype shows this idea can work in practice.
What this means in practice
- •For cloud infrastructure teams: Automatically translate high-level user goals into deployment plans to optimize microservice placement and meet service targets across diverse locations.
- •For telecommunications operators: Deploy distributed services more effectively by interpreting customer needs in plain language to improve latency and availability over broad networks.
Authors
Koushikur Islam, Guilherme Da Cunha Rodrigues, Bahman Javadi, Rodrigo N. Calheiros
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 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.