Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2025

Abstract

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, further exacerbating these issues. To address these concerns, we propose Smart HPA and ProSmart HPA, reactive and proactive resource-efficient horizontal pod auto-scalers respectively. Smart HPA employs a reactive scaling policy that facilitates resource exchange among microservices, optimizing auto-scaling in resource-constrained environments. For ProSmart HPA, we develop a machine-learning-driven resource-efficient scaling policy that proactively manages resource demands to address delays caused by microservice pod startup and termination, while enabling preemptive resource sharing in resource-constrained environments. Our experimental results show that Smart HPA outperforms the Kubernetes baseline HPA, while ProSmart HPA exceeds both Smart HPA and Kubernetes HPA by reducing resource overutilization, overprovisioning, and underprovisioning, and increasing resource allocation to microservice applications.

Keywords

Software architecture, Auto-scaling, Microservices, Resource management, Self-adaptation, Kubernetes

Discipline

Artificial Intelligence and Robotics | Software Engineering

Areas of Excellence

Digital transformation

Publication

Journal of Systems and Software

Volume

225

Issue

C

First Page

1

Last Page

18

ISSN

0164-1212

Identifier

10.1016/j.jss.2025.112390

Publisher

Elsevier

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