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.

本文对自适应自感知云自适应系统 (SSCAS) 进行了全面的综述和分类，旨在提供更深入的分析和见解，以及未来工作中值得研究的方向，从而为云中更智能的自动缩放系统的工程奠定基础。