Genetic Programming for Self-Adaptive Auto-Scaling of Microservices

TL;DR

AutoSLO is a learning-based, self-adaptive framework using genetic programming to optimize microservice auto-scaling, reducing resource use while maintaining service-level objectives.

Contribution

It introduces AutoSLO, a novel genetic programming approach for proactive, self-adaptive auto-scaling in microservices, outperforming reactive methods.

Findings

AutoSLO reduces resource usage significantly.

AutoSLO maintains low SLO violation frequency.

AutoSLO quickly resolves SLO violations.

Abstract

Microservice architecture is widely adopted in modern systems, where auto-scaling is critical for satisfying service-level objectives (SLOs). However, determining optimal scaling for microservices is difficult, and reactive resource allocation often leads to costly over- or under-provisioning. We propose AutoSLO, a learning-based, self-adaptive scaling framework that dynamically adjusts microservice replicas to meet SLOs while minimizing resource usage. AutoSLO uses a continuous monitoring-adaptation feedback loop and leverages genetic programming to learn and evolve scaling logic, enabling the deployed microservice system to proactively prevent SLO violations rather than repeatedly searching for one-off scaling actions. We evaluate AutoSLO on two case-study systems -- an online shopping platform and a chatbot based on large language models -- and show that this framework substantially reduces resource usage while maintaining a low frequency of SLO violations, all of which are resolved within a short time window.