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

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journal contribution
posted on 19.09.2019, 13:34 by Tao ChenTao Chen, Rami Bahsoon, Xin Yao
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.

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 Surveys

Volume

51

Issue

3

Publisher

Association for Computing Machinery (ACM)

Version

VoR (Version of Record)

Rights holder

© The Authors

Acceptance date

31/03/2018

Publication date

2018-07-16

Copyright date

2018

ISSN

0360-0300

eISSN

1557-7341

Language

en

Depositor

Dr Tao Chen

Article number

61