survey

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

Authors:

Xin YaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 3

Article No.: 61, Pages 1 - 40

https://doi.org/10.1145/3190507

Published: 12 June 2018 Publication History

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 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 Self-adaptive 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.

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Index Terms

A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems
1. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance
    2. Software system structures
      1. Distributed systems organizing principles
        Cloud computing

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 3

May 2019

796 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3212709

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2018 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2018

Accepted: 01 February 2018

Revised: 01 January 2018

Received: 01 April 2017

Published in CSUR Volume 51, Issue 3

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https://doi.org/10.4018/979-8-3693-7190-9.ch003
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