survey

A Survey of Profit Optimization Techniques for Cloud Providers

Authors:

Keqin LiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 2

Article No.: 26, Pages 1 - 35

https://doi.org/10.1145/3376917

Published: 20 March 2020 Publication History

Abstract

As the demand for computing resources grows, cloud computing becomes more and more popular as a pay-as-you-go model, in which the computing resources and services are provided to cloud users efficiently. For cloud providers, the typical goal is to maximize their profits. However, maximizing profits in a highly competitive cloud market is a huge challenge for cloud providers. In this article, a survey of profit optimization techniques is proposed to increase cloud provider profitability through service quality improvement, service pricing, energy consumption reduction, and virtual network function (VNF) deployment. The strategy of improving user service quality is discussed first, followed by the pricing strategy for cloud resources to maximize revenue. Then, this article summarizes the techniques for cloud data centers to reduce server power consumption. Finally, various heuristic algorithms for VNF deployment in the cloud are further described to reduce the cost of cloud providers while maintaining performance. We classify research works based on components of profit and methods used to demonstrate similarities and differences in these studies. We hope this survey will provide researchers with insights into cloud profit optimization techniques.

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Index Terms

A Survey of Profit Optimization Techniques for Cloud Providers
1. Networks
  1. Network properties
    1. Network reliability

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ACM Computing Surveys Volume 53, Issue 2

March 2021

848 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3388460

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 March 2020

Accepted: 01 December 2019

Revised: 01 November 2019

Received: 01 June 2019

Published in CSUR Volume 53, Issue 2

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National Key Research and Development Program of China
ECNU XingFuZhiHua Program
National Natural Science Foundation of China

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