# A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems

arXiv: https://arxiv.org/abs/1609.03590 · HTML: https://mlsanity.com/paper/1609.03590/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2016.

## Built on by (most cited first, 18 of 18)

- [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes](https://mlsanity.com/paper/2203.15988/) (2022)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [The Vision of Self-Evolving Computing Systems](https://mlsanity.com/paper/2204.06825/) (2022)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud](https://mlsanity.com/paper/2311.12864/) (2023)
- [Awareness requirement and performance management for adaptive systems: a survey](https://mlsanity.com/paper/2302.05518/) (2023)
- [Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices](https://mlsanity.com/paper/2510.06882/) (2025)
- [Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment](https://mlsanity.com/paper/2110.03431/) (2021)
- [STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices](https://mlsanity.com/paper/2604.26422/) (2026)
- [An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism](https://mlsanity.com/paper/2607.15511/) (2026)

## Abstract (arXiv)

Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud software and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not mature to a state that it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this field. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud.