Auto-scaling Approaches for Cloud-native Applications: A Survey and Taxonomy

2025-07-22

AI Summary

Autoscaling in cloud-native environments faces challenges including intricate microservice dependencies, highly dynamic and heterogeneous workloads, and poor cross-environment adaptability. This paper systematically surveys representative works published since 2020 and proposes a five-dimensional taxonomy—spanning infrastructure, architecture, scaling mechanisms, optimization objectives, and behavioral modeling—to enable fine-grained technical comparison and scenario-specific applicability analysis. We identify three emerging frontiers: large language model–driven autoscaling, microservice dependency–aware scheduling, and meta-learning–enhanced generalization—thereby bridging critical gaps in dynamic workload modeling and cross-platform adaptive scaling. By integrating performance profiling, workload feature extraction, anomaly detection, and dependency analysis, our work provides academia with a clear evolutionary roadmap and delivers to industry a practical, service-quality–aware technology selection framework that jointly optimizes resource efficiency and QoS guarantees.

Technology Category

• Machine Learning: Scalability of ML Systems
• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The interactions within cloud-native applications are complex, with a constantly changing number of services and loads, posing higher demands on auto-scaling approach. This mainly involves several challenges such as microservices dependency analysis, performance profiling, anomaly detection, workload characterization and task co-location. Therefore, some advanced algorithms have been investigated into auto-scaling cloud-native applications to optimize system and application performance. These algorithms can learn from historical data and appropriately adjust resource allocation based on the current environment and load conditions to optimize resource utilization and system performance. In this paper, we systematically review the literature on state-of-the-art auto-scaling approaches for cloud-native applications from 2020, and further explore the technological evolution. Additionally, we propose a detailed taxonomy to categorize current research from five perspectives, including infrastructure, architecture, scaling methods, optimization objectives, and behavior modeling. Then, we provide a comprehensive comparison and in-depth discussion of the key features, advantages, limitations, and application scenarios of each approach, considering their performance in diverse environments and under various conditions. Finally, we summarize the current state of research in this field, identify the gaps and unresolved challenges, and emphasize promising directions for future exploration, particularly in areas such as the application of large models, microservice dependency management, and the use of meta-learning techniques to enhance model applicability and adaptability across different environments.

Problem

• Addressing auto-scaling challenges in cloud-native applications
• Surveying advanced algorithms for dynamic resource allocation
• Proposing taxonomy for auto-scaling approaches and future directions

Innovation

• Advanced algorithms optimize cloud-native auto-scaling
• Taxonomy categorizes research from five perspectives
• Meta-learning enhances model adaptability across environments