Enabling Autonomic Microservice Management through Self-Learning Agents

TL;DR

This paper introduces ServiceOdyssey, a self-learning agent system that autonomously manages microservices by applying curriculum learning and iterative exploration, reducing reliance on human input and static documentation.

Contribution

It presents a novel self-learning agent framework for microservice management that adapts through exploration without prior service-specific knowledge.

Findings

Prototype with Sock Shop microservice validates approach

Reduces need for human intervention in microservice management

Demonstrates effective autonomous adaptation in operational environments

Abstract

The increasing complexity of modern software systems necessitates robust autonomic self-management capabilities. While Large Language Models (LLMs) demonstrate potential in this domain, they often face challenges in adapting their general knowledge to specific service contexts. To address this limitation, we propose ServiceOdyssey, a self-learning agent system that autonomously manages microservices without requiring prior knowledge of service-specific configurations. By leveraging curriculum learning principles and iterative exploration, ServiceOdyssey progressively develops a deep understanding of operational environments, reducing dependence on human input or static documentation. A prototype built with the Sock Shop microservice demonstrates the potential of this approach for autonomic microservice management.