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Download fileA survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems
journal contribution
posted on 2019-09-19, 13:34 authored by Tao Chen, Rami Bahsoon, Xin YaoAutoscaling 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.
Funding
Ministry of Science and Technology of China (Grant No. 2017YFC0804003)
Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284)
EPSRC (Grant No. EP/J017515/01 and EP/K001523)
History
School
- Science
Department
- Computer Science
Published in
ACM Computing SurveysVolume
51Issue
3Publisher
Association for Computing Machinery (ACM)Version
- VoR (Version of Record)
Rights holder
© The AuthorsAcceptance date
2018-03-31Publication date
2018-07-16Copyright date
2018ISSN
0360-0300eISSN
1557-7341Language
- en