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 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…
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Taxonomy
TopicsCloud Computing and Resource Management · Data Stream Mining Techniques
