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

TL;DR

This paper surveys the current state of self-aware and self-adaptive cloud autoscaling systems, providing a taxonomy, analyzing research results, and discussing open challenges and future directions for more reliable and intelligent cloud autoscaling.

Contribution

It offers a comprehensive taxonomy and analysis of SSCAS research, highlighting gaps and future research directions in the development of reliable, intelligent autoscaling systems.

Findings

Existing SSCAS are not yet mature for reliable deployment.

The survey identifies key open challenges in SSCAS development.

Promising future research directions are outlined for improving SSCAS.

Abstract

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.