Auto-scaling Approaches for Microservice Applications: A Survey and Taxonomy

TL;DR

This paper surveys recent auto-scaling strategies for microservice applications, focusing on service dependency management, workload adaptation, and optimization objectives, highlighting the shift to service-level, dependency-aware auto-scaling since 2018.

Contribution

It provides a comprehensive taxonomy and comparison of state-of-the-art auto-scaling approaches for microservices, emphasizing their features, strengths, and limitations since 2018.

Findings

Service-level auto-scaling has become prevalent since 2018.

Dependency-aware strategies improve resource efficiency and SLA compliance.

Different approaches vary in performance across environments.

Abstract

Microservice applications are created as loosely coupled application components and they leverage cloud elasticity to reduce costs and increase development speed. However, microservice applications exhibit complex interactions among dynamically evolving services and highly variable workloads, posing significant challenges to auto-scaling mechanisms. Key issues include service dependency management, performance profiling, anomaly detection, workload characterization, and fine-grained resource allocation. To address these challenges, recent auto-scaling approaches leverage historical and runtime data to adapt resource provisioning and optimize system efficiency. Since 2018, marked by the graduation of Kubernetes as the first Cloud Native Computing Foundation (CNCF) project, microservice applications have been widely deployed on standardized orchestration platforms, fundamentally shifting auto-scaling from coarse-grained to service-level, dependency-aware strategies. Accordingly, this paper surveys state-of-the-art auto-scaling approaches for microservice applications since 2018 and presents a taxonomy along five dimensions: infrastructure, architecture, scaling methods, optimization objectives, and behavior modeling. These perspectives collectively target key objectives, including resource efficiency, cost efficiency, and Service Level Agreement (SLA) assurance, aiming to balance system optimization with SLA compliance. We further present a comprehensive comparison and in-depth analysis of representative approaches, examining their core features, strengths, limitations, and applicable scenarios, as well as their performance across diverse environments and workload conditions.