Abstract.

This paper examines the current state and factors that influence the diffusion of cloud computing in the regions of the world, analyzes approaches to modeling the diffusion of information technologies in general and cloud computing in particular, compares the forecasts of the cloud market of leading analytical companies with the data of patent analysis. The Bass diffusion model was estimated to define time and magnitude of peak of cloud traffic in different regions of the world. The results of approximating the share of cloud computing in the IT services market in Russia, obtained by the author using various diffusion models, is compared, the influence of external factors on the speed of cloud computing in Russia is estimated.

Keywords:

cloud computing, diffusion models, patent analysis, virtualization, traffic, public clouds

PP. 24-36.

References

1. Mell, P., & Grance, T. 2011. The NIST Definition of Cloud Computing. Computer Security Division, Information Technology  Laboratory, National Institute of Standards and Technology, United States Department of Commerce. Gaithersburg, MD 20899 -8930: National Institute of Standards and Technology. Retrieved January 28, 2014, from  http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf.
2. Five Key Take-Aways From North Bridge’s Future Of Cloud Computing Survey, 2015 Available at:  https://softwarestrategiesblog.com/tag/cloud-computing-forecasts/ (accessed April 12, 2017).
3. Gartner Says Worldwide Public Cloud Services Market to Grow 18 Percent in 2017. 2017. Available at:  http://www.gartner.com/newsroom/id/3616417  (accessed April 26, 2017).
4. Finos R. 2015. Public Cloud Market Forecast 2015-2026. – Wikibon, 2015. Available at: https://wikibon. com/public-cloud-market-forecast-2015-2026/ (accessed June 26, 2017).
5. 10 Compelling Keys for Business Success with Google. 2014. Available at:  http://www.inescalate.com/wpcontent/uploads/2014/02/10-Compelling-Keys-for-Business-Success-with-Google-Inescalate-2.pdf  (accessed June 26, 2017).
6. CNews. Oblachnye servisy 2016. [Cloud Services 2016] Available at: http://www.cnews.ru/reviews/oblachnye_servisy_2016  (accessed April 4, 2017).
7. Russia Cloud Services Market 2015-2019 Forecast and 2014 Analysis. 2015. IDC Available at:
http://idcrussia.com/ru/research/published-reports/61077-russia-cloud-services-market-2015-2019-forecast-and- 2014analysis/2-abstract (accessed June 17, 2017)
8. Nastupivshij 2016 god mozhet projti pod znakom virtual’noj real’nosti. 2016. Virtualizatsiya. Oblachnye struktury. Sistemy  khraneniya dannykh [Virtualization. Cloud structures. Storage systems]. 4 (70):1– 6.
9. Grebnev, E. Oblachnye servisy. Vzglyad iz Rossii. [Cloud services. View from Russia]. М.: CNews, 2011. 282 p.
10. Etro, F. 2010. The Economic Consequences of the Diffusion of Cloud Computing, Chapter 1.9 in Global Information  Technology Report 2009-2010, World Economic Forum, Geneve, 107-112.
11. Delforge P. 2014. America’s Data Centers Are Wasting Huge Amounts of Energy. National Resources Defense Council, Issue  Brief 14-08-A. Available at: https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts- energy (accessed April 26, 2017).
12. Varshavskij L.E. 2013. Problemy povysheniya ehnergoehffektivnosti apparatnykh sredstv v oblasti informatsionnykh  tekhnologij [Problems of energy efficiency increase of information technologies equipment infrastructure]. Trudy ISА RАN. 63.
3:3–19.
13. Buyya R., Broberg J., Goscinski A.M. 2010. Cloud Computing Principles and Paradigms. WILEY Publication, 2010. 674 P.
14. Oludele, A., Ogu, E.C., Shade, K., Chinecherem, U. 2014. On the Evolution of Virtualization and Cloud Computing: A  Review. Journal of Computer Sciences and Applications, Vol. 2, No. 3: 40-43.
15. Dubinina M.G. 2015. Issledovanie sovremennykh podkhodov k modelirovaniyu protsessov rasprostraneniya tekhnologij v  naukoemkikh otraslyakh [A study of current approaches to modeling the diffusion of technologies in high-tech industries].
Trudy ISА RАN. 65. 3: 43-54.
16. Song, Y., Lee, S., Zo, H., Lee, H. 2015. A hybrid Bass–Markov model for the diffusion of a dual-type device-based  telecommunication service: The case of WiBro service in Korea. Computers & Industrial Engineering. 79: 85–94.
17. Kim, N., Bridges, E., Srivastava, R. 1999. A simultaneous model for innovative product category sales diffusion and  competitive dynamics. International Journal of Research in Marketing. 16:562–583.
18. Floyd, A. 1968. A methodology for trend forecasting of figures of merit In Technological Forecasting for Industry and  Government: Methods and Applications, James R.Bright (ed.), Englewood Cliffs, NJ:Prentice-Hall.
19. Easingwood, C., Mahajan, V., Muller, E. 1981. A Nonsymmetric Responding Logistic Model for Forecasting Technological  Substitution. Technological Forecasting and Social Change. 20:199-213.
20. Skiadas, C.H. 1985. Two Generalized Rational Models for Forecasting Innovation Diffusion. Technological Forecasting and  Social Change 27:39-61.
21. Sharif, M.N.,Islam, M.N. 1980. The Weibull Distribution as a General Model for Forecasting Technological Change.  Technological Forecasting and Social Change. 18: 247-256.
22. Mahajan, V., Peterson, R.A. 1985. Models for Innovation Dlflision. Beverly Hills, CA: Sage, 1985.
23. Skiadas, C.H. 1986. Innovation Diffusion Models Expressing Asymmetry and/or Positively or Negatively Influencing Forces.  Technological Forecasting and Social Change. 30: 313-330.
24. Coleman, J.S., Katz, E., & Menzel, H. 1966. Medical innovation: Diffusion of a medical drug among doctors. Indianapolis:  Bobbs-Merrill.
25. Giovanis, A.N., Skiadas, C.H. 2007. A new modeling approach investigating the diffusion speed of mobile  telecommunication services in EU-15. Comput. Econ.29:97–106.
26. Varshavskij, А.E. 2009. Problemnye innovatsii v obrabotke dannykh bez polnotsennoj informatsii ob ob”ekte issledovaniya i  ogranichenij na oblast’ primeneniya [Questionable Innovations in Data Processing with Incomplete Information about the  Analyzed System in Absence of Applications Limitations]. Prikladnaya ehkonometrika [Applied Econometrics] №4 (16): 116-133.
27. Sheikh, N., Gomez, F. A., Cho, Y. and Siddappa J. 2011. Forecasting of advanced electronic packaging technologies using  bibliometric analysis and Fisher-Pry diffusion model Picmet: Portland International Center For Management of Engineering and  Technology, Proceedings.
28. Adamuthe, A.C., Tomake, J.V., Thampi, G.T. 2014. Technology Forecasting: The Case of Cloud Computing and Sub- Technologies. International Journal of Computer Applications .V.106.2:14 – 19.
29. WIPO. Available at: https://patentscope.wipo.int/ search/en/result.jsf (accessed June 28, 2017).
30. Japan Platform for the Patent Information J-PlatPat. Available at: https://www19.j-platpat. inpit.go.jp/PA1/cgi-bin/PA1LIST  (accessed June 24, 2017).
31. Sizing The Cloud. Forrester Research Inc. Available at:: http://licensinglive.com/wp-content/ uploads/2012/03/Hybrid-Customer-Insight-Data-Collection-and-Analysis-from-on-premise-in-thecloud.pdf (accessed June 28, 2017).
32. Smart Cloud Study Group Report. 2010. Ministry of Internal Affairs and Communications. Available at: http://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/councilreport/pdf/100517_1.pdf (accessed July 1, 2017).
33. Cisco Global Cloud Index: Forecast and Methodology, 2015–2020 White Paper. – Cisco. – 2016. Available at:  http://www.cisco.com/c/dam/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf  (accessed June 28, 2017).
34. Bass, F. 1969. A New Product Growth for Model Consumer Durables. Management Science. 15 (5):215-227.
35. Russia Cloud Services Market 2016–2020 Forecast and 2015 Vendor Shares. 2016. IDC. Available at::  http://idcrussia.com/ru/about-idc/presscenter/64185-press-release (дата обращения – 28.06.17).
36. Gompertz, B. 1825. On the Nature of the Function Expressive of the Law of Human Mortality, and a New Mode of  Determining the Value of Life Contingencies. Philosophical Transactions of the Royal Society of London. 115: 513-585.
37. Rodbard, D. 1974. Statistical quality control and routine data processing for radioimmunoassays and immunoradiometric  assays.Clin Chem. 20(10):1255–1270.
38. Newell, J., Genschel, U., Zhang, N. 2014. Media Discontinuance: Modeling the Diffusion “S” Curve to Declines in Media Use.  Journal of Media Business Studies. 11(4):1-35.
39. WorldBank Infrastructure Indicators. Available at: http://data.worldbank.org/indicator/IT.NET.SECR?view=chart (accessed  July 1, 2017).
40. Indikatory informatsionnogo obshhestva: 2016 : statisticheskij sbornik / G. I. Аbdrakhmanova, L. M. Gokhberg, M. А.  Kevesh i dr.; [Indicators of the Information Society: 2016: Statistical Digest] Nats. issled.un-t «Vysshaya shkola ehkonomiki».
– M.: NIU VSHEH, 2016. – 304 s.