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A study of revenue cost dynamics in large data centers: a factorial design approach

Published: 22 March 2017 Publication History

Abstract

Revenue optimization of large data centers is an open and challenging problem. The intricacy of the problem is due to the presence of too many parameters posing as costs or investment. This paper proposes a model to optimize the revenue in cloud data center and analyzes the model, revenue and different investment or cost commitments of organizations investing in data centers. The model uses the Cobb-Douglas production function to quantify the boundaries and the most significant factors to generate the revenue. The dynamics between revenue and cost is explored by designing an experiment (DoE) which is an interpretation of revenue as function of cost/investment as factors with different levels/fluctuations. Optimal elasticity associated with these factors of the model for maximum revenue are computed and verified. The model response is interpreted in light of the business scenario of data centers.

References

[1]
Google, How much do Google data centers cost?, {WWW document}. Retrieved Sepetember 10, 2016 from http://www.datacenterknowledge.com/google-data-center-faq-part-2/.
[2]
Apple, How Big is Apple's North Carolina Data Center? {WWW document}, Retrieved Sepetember 13, 2016 from http://www.datacenterknowledge.com/the-apple-data-center-faq/.
[3]
Facebook, How much Does Facebook Spend on its Data Centers? {WWW document}, Retrieved Sepetember 19, 2016 from http://www.datacenterknowledge.com/the-facebook-data-center-faq-page-three/, as accessed on 26 February 2016
[4]
Albert Greenberg, James Hamilton, David A.Maltz and Praveen Patel. 2009. The Cost of a Cloud: Research Problems in Data Center Networks, Computer Communication Review, 39, 1, 68--73, 2009
[5]
Mahdi Ghamkhari and Hamed MohsenianRad, Energy and Performance Management of Green data centers. 2013. A Profit Maximization Approach, IEEE transactions on Smart Grid, 4, 2, 1017 -- 1025, 2013
[6]
Zhi Chen, Lei Wu and Zuyi Li. 2014. Electric Demand Response Management for Distributed Large Scale Internet Data centers,IEEE transactions on Smart Grid, 5, 2, 651 -- 661, March 2014
[7]
Adel Nadjaran Toosi, Kurt Vanmechelen, Kotagiri Rammohanrao and Rajkumar Buyya. 2015. Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets, IEEE transactions on Cloud Computing, 3, 3, 261 -- 274, July-September 2015
[8]
Yuan Zhang, Xiaoming Fu, K K and Ramakrishnan. 2014. Fine-Grained Multi-Resource Scheduling in Cloud Data Centers, 2014 IEEE 20th International Workshop on Local & Metropolitan Area Networks (LANMAN),
[9]
Liaping Zhao, Liangfu Lu, Zhou Jin and Ce Yu. 2015. Online Virtua Machine Placement for Increasing Cloud Providers Revenue IEEE Transactions on Services Computing, 1, 1, PrePrints,
[10]
Snehanshu Saha, Jyotirmoy Sarkar, Anand MN, Avantika Dwivedi, Nandita Dwivedi, Ranjan Roy and Shirisha Rao. 2016. A Novel Revenue Optimization Model to address the operation and maintenance cost of a Data Center. Journal of Cloud Computing, Advances, Systems and Applications. 5, 1, 1--23, 2016
[11]
Cobb Douglas Production Function {WWW document}. Retrieved Sepetember 2, 2016 from http://economicpoint.com/production-function/cobb-douglas
[12]
Table 1-Dataset, https://github.com/swatigambhire/demorepo, as accessed on 26 February 2016
[13]
Raj Jain. 1991. The Art of Computer System Performance Analysis. 1--720, Wiley; ISBN: 978-0-471-50336-1

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cover image ACM Other conferences
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing
March 2017
1349 pages
ISBN:9781450347747
DOI:10.1145/3018896
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2017

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Author Tags

  1. cloud computing
  2. cobb-douglas production function
  3. data center
  4. design of experiment (DoE)
  5. infrastructure as a service (IaaS)
  6. modeling
  7. optimization
  8. replication

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ICC '17

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ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;
Overall Acceptance Rate 213 of 590 submissions, 36%

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