A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems


Research output: Journal PublicationsJournal Article (refereed)peer-review

72 Citations (Scopus)


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 a modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, the cloud autoscaling system has been engineered as one of the most complex, sophisticated, and intelligent artifacts created by humans, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. Yet the existing Self-aware and Selfadaptive Cloud Autoscaling System (SSCAS) is not at a state where 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. © 2018 ACM.
Original languageEnglish
Article number61
JournalACM Computing Surveys
Issue number3
Early online date12 Jun 2018
Publication statusPublished - 31 May 2019
Externally publishedYes

Bibliographical note

This work was supported by the Ministry of Science and Technology of China (Grant No. 2017YFC0804003), Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284), and EPSRC (Grant No. EP/J017515/01 and EP/K001523).


  • Auto-scaling
  • Cloud computing
  • Distributed systems self-aware systems
  • Resources provisioning
  • Self-adaptive systems


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