Auto-scaling Web Applications in Clouds: A Taxonomy and Survey
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Abstract (arXiv)
Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on-demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this paper, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 1; 2026: 2.
6 papers.
- 2023AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetescited by 9
- 2023A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functionscited by 5
- 2021Sinan: Data-Driven, QoS-Aware Cluster Management for Microservicescited by 0
- 2025Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scalingcited by 0
- 2026Incentives and Evidence in Learned Service Orchestrationcited by 0
- 2026ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDBcited by 0
- 2026ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB8 Aug 2026
- 2026Incentives and Evidence in Learned Service Orchestration15 Jun 2026
- 2025Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling18 Aug 2025
- 2023A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions11 Aug 2023
- 2023AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes7 Mar 2023
- 2021Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices27 May 2021