research-article

Characterizing the Usage Intensity of Public Cloud

Authors:

Aadharsh Kannan,

Jacob LaRiviere,

R. Preston McAfeeAuthors Info & Claims

ACM Transactions on Economics and Computation (TEAC), Volume 9, Issue 3

Article No.: 15, Pages 1 - 18

https://doi.org/10.1145/3456760

Published: 29 May 2021 Publication History

Abstract

This article uses precise and novel data on country-level Cloud IaaS and PaaS revenue to measure the intensive margin of technology diffusion across countries and within countries over time. We horse race diffusion models and find that cloud diffusion exhibits both Log-Log and Logistic Growth patterns. We use cross validation on nearly 100 features to determine what correlates with cross-country differences. We find that increases in features impacting Gross Domestic Product, Internet Connectivity, and Human Capital are associated with increases in intensity of cloud adoption. We finally compare the relative impacts of these variables using a random coefficients model. Although correlative, our algorithmic research design motivates data-driven hypothesis generation and further causal work regarding how policymakers can encourage more cloud computing adoption and technology adoption more broadly.

References

[1]

Patricia S. Abril and Robert Plant. 1845. Mathematical researches into the law of population growth increase. Nouveaux Memoires de l’Academie Royale des Sciences et Belles-Lettres de Bruxelles 18 (1845), 1–42.

[2]

R. U. Ayres. 1989. Technological Transformations and Long Waves. Ph.D. Dissertation. International Institute for Applied Systems Analysis, Laxenburg, Austria. http://webarchive.iiasa.ac.at/Admin/PUB/Documents/RR-89-001.pdf.

[3]

Heiko Bauke. 2007. Parameter estimation for power-law distributions by maximum likelihood methods. European Physical Journal B 58, 2 (Aug. 2007), Article 2, 6 pages.

[4]

Alexandre Belloni and Victor Chernozhukov. 2013. Least squares after model selection in high-dimensional sparse models. Bernoulli 19, 2 (2013), 521–547.

[5]

Jess Benhabib and Mark Spiegel. 2005. Human capital and technology diffusion. In Handbook of Economic Growth,Philippe Aghion and Steven Durlauf (Eds.). Vol. 1, Part A. Elsevier, 935–966. https://EconPapers.repec.org/RePEc:eee:grochp:1-13.

[6]

Aaron Clauset, Cosma Rohilla, and M. E. J. Newman. 2009. Power law distribution in empirical data. Society for Industrial and Applied Math 51, 4 (Nov. 2009), Article 5, 43 pages.

Digital Library

[7]

Diego Comin and Bart Hobijn. 2010. An exploration of technology diffusion. American Economic Review 100, 5 (2010), 2031–59.

[8]

Diego A. Comin and Marti Mestieri Ferrer. 2013. If Technology Has Arrived Everywhere, Why Has Income Diverged?Working Paper 19010. National Bureau of Economic Research.

[9]

Amazon’s Quarterly Revenue Filing. 2017. Securities and Exchange Commission Filings. Technical Report. Amazon. http://phx.corporate-ir.net/phoenix.zhtml?c=97664&p=irol-sec&control_selectgroup=Annual20Filings.

[10]

Garrett Fitzmaurice, Marie Davidian, Geert Verbeke, and Geert Molenberghs (Eds.). 2009. Longitudinal Data Analysis (2nd ed.). Chapman & Hall CRC, Boca Raton, FL, 309–362. www.stat.ncsu.edu/people/davidian/courses/st732/notes/chap9.pdf

[11]

Gartner Forecast. 2017. Gartner Says WW Public Cloud Services Market to Grow 18 Percent in 2017. Technical Report. Gartner. http://www.gartner.com/newsroom/id/3616417.

[12]

World Economic Forum. 2017–2018. The Global Competitiveness Report 2017–2018. Technical Report. World Economic Forum. https://www.weforum.org/reports/the-global-competitiveness-report-2017-2018.

[13]

John Fox and Sanford Weisberg. 2010. An R Companion to Applied Regression (2nd ed.). SAGE, Washington DC, Appendix. http://socserv.mcmaster.ca/jfox/Books/Companion/appendix/Appendix-Nonlinear-Regression.pdf.

[14]

Jordan Novet. 2017. Amazon’s cloud growth might be slowing, but it’s still way ahead of everybody else. CNBC. Retrieved September 27, 2017 from https://www.cnbc.com/2017/07/28/aws-still-rules-the-cloud-despite-growth-slump.html.

[15]

Sungho Park and Sachin Gupta. 2009. Simulated maximum likelihood estimator for the random coefficient logit model using aggregate data. Journal of Marketing Research 46, 4 (2009), 531–542. http://www.jstor.org/stable/20618914.

[16]

W. Craig Riddell and Xueda Song. 2017. The role of education in technology use and adoption: Evidence from the Canadian workplace and employee survey. ILR Review 70, 5 (2017), 1219–1253.

[17]

Jonathan Skinner and Douglas Staiger. 2005. Technology Adoption from Hybrid Corn to Beta Blockers. Working Paper 11251. National Bureau of Economic Research.

[18]

Tanner Sorensen and Shravan Vashisht. 2016. Bayesian linear mixed models using Stan. Quantitative Methods for Psychology 12, 3 (June 2016), 1–30.

[19]

Jeff Tayman and David A. Swanson. 1999. On the validity of MAPE as a measure of population forecast accuracy. Population Research and Policy Review 18, 4 (Aug. 1994), 299–322.

[20]

Shuang Wang, Jacob LaRiviere, and Aadharsh Kannan. 2019. Spatial competition and missing data: An application to cloud computing. In Proceedings of the 2019 NBER Technology Workshop.

[21]

F. Welch. 1970. Education in production. Journal of Political Economy 78, 1 (1970), 35–59. https://EconPapers.repec.org/RePEc:ucp:jpolec:v:78:y:1970:i:1:p:35-59.

Cited By

Lanciano GAndreoli RCucinotta TBacciu DPassarella A(2023)A 2-Phase Strategy for Intelligent Cloud OperationsIEEE Access10.1109/ACCESS.2023.331221811(96841-96853)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3312218
Kaur NKumar AKumar R(2022)TRAP: task-resource adaptive pairing for efficient scheduling in fog computingCluster Computing10.1007/s10586-022-03641-z25:6(4257-4273)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10586-022-03641-z

Index Terms

Characterizing the Usage Intensity of Public Cloud
1. Social and professional topics
  1. Professional topics
    1. Computing industry
      1. Industry statistics

Recommendations

Comparing public and private iaas cloud models
RIIT '14: Proceedings of the 3rd annual conference on Research in information technology

To better understand the advantages and disadvantages of deploying a private cloud or employing a public cloud for computing resources, a fully featured private cloud of infrastructure as a service (IaaS) was built from bare metal servers using Xen ...
Cloud service engineering
ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2

Building on compute and storage virtualization, Cloud Computing provides scalable, network-centric, abstracted IT infrastructure, platforms, and applications as on-demand services that are billed by consumption. Cloud Service Engineering is the ...
Optimal Resource Usage in Multi-Cloud Computing Environment

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. This paper presents an optimal resource management ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Economics and Computation

ACM Transactions on Economics and Computation Volume 9, Issue 3

September 2021

181 pages

ISSN:2167-8375

EISSN:2167-8383

DOI:10.1145/3468852

Editors:
David Pennock
Microsoft, USA
,
Ilya Segal
Stanford University, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2021

Accepted: 01 December 2020

Revised: 01 April 2020

Received: 01 October 2018

Published in TEAC Volume 9, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
142
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)1

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lanciano GAndreoli RCucinotta TBacciu DPassarella A(2023)A 2-Phase Strategy for Intelligent Cloud OperationsIEEE Access10.1109/ACCESS.2023.331221811(96841-96853)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3312218
Kaur NKumar AKumar R(2022)TRAP: task-resource adaptive pairing for efficient scheduling in fog computingCluster Computing10.1007/s10586-022-03641-z25:6(4257-4273)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10586-022-03641-z

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents