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

Minxian Xu, Junhan Liao, Linfeng Wen, Huaming Wu, Kejiang Ye, Rajkumar Buyya, Chengzhong Xu

TL;DR

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, aiming to balance system optimization with SLA compliance.

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.

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

TL;DR

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, aiming to balance system optimization with SLA compliance.

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.

Paper Structure

This paper contains 26 sections, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. The need for more intelligent and accurate auto-scaling strategies
  3. The demand to manage resource contention and inter-application performance interference in co-located environments
  4. The requirement to incorporate for inter-dependencies and invocation relationships among microservices
  5. The need for more comprehensive and refined metrics and monitoring
  6. Related Work
  7. Background
  8. Microservice-Oriented Architecture
  9. Auto-scaling Technology
  10. A Taxonomy of Auto-Scaling Technologies for Microservice Applications
  11. Infrastructure
  12. Application Architecture
  13. Scaling Methods
  14. Scaling Objectives
  15. Behavior Modeling
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Microservice Application Deployment Architecture and A Typical Application Example.
  • Figure 2: Taxonomy of Auto-scaling Approaches for Microservice Applications.
  • Figure 3: Evolution of Auto-scaling Approaches for Microservice Applications.