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The Internet: Explaining ICT Service Demand in Light of Cloud Computing Technologies

Chapter

Abstract

Cloud Computing (CloudC) is one of the most prominent recent trends in the digital communications sector and represents a paradigm shift within the ICT industry. The supply of popular applications, such as cloud storage and cloud video streaming, has caused a surge in the demand for CloudC services, which offer the advantages of low economic cost, high data transfer speeds, and improved mobility, security, scalability, and multi-tenancy. In this chapter, we investigate the circumstances under which this new CloudC infrastructure is likely to reduce energy use of our new digital lifestyle, or when it simply catalyses a rebound effect that could hamper ICT-related energy savings. We classify CloudC rebound effects as either direct or indirect rebound effects, and we discuss the differences and overlap between rebound effects, enabling effects, and transformational effects. An understanding of these differences is important for understanding energy use associated with CloudC.

Keywords

Cloud computing Rebound effects Enabling effects Transformational effects 

References

  1. A.S.G. Andrae, Method based on market changes for improvement of comparative attributional life cycle assessments. Int. J. Life Cycle Assess. 20, 263–275 (2015)CrossRefGoogle Scholar
  2. A.S.G. Andrae, T. Edler, On global electricity usage of communication technology: trends to 2030. Challenges 6, 117–157 (2015)CrossRefGoogle Scholar
  3. P.M. Corcoran, A.S.G. Andrae, Emerging trends in electricity consumption for consumer ICT. National University of Ireland, Galway, Connacht, Ireland, Tech. Rep. Available via National University of Ireland (2013). http://vmserver14.nuigalway.ie/xmlui/handle/10379/3563. Accessed 21 Sept 2015
  4. J.L. Funk, IT and sustainability: new strategies for reducing carbon emissions and resource usage in transportation. Telecom. Policy 39, 861–874 (2015)CrossRefGoogle Scholar
  5. R. Galvin, The ICT/electronics question: Structural change and the rebound effect. Ecol. Econ. 120, 23–31 (2015)CrossRefGoogle Scholar
  6. GeSI, #SMARTer2030: ICT solutions for 21st century challenges. Available via GeSI (2015). http://smarter2030.gesi.org/downloads/Full_report.pdf. Accessed 21 Sept 2015
  7. L.A. Greening, D.L. Greene, C. Difiglio, Energy efficiency and consumption—the rebound effect—a survey. Energy. Policy. 28, 389–401 (2000)Google Scholar
  8. J. Jenkins, T. Nordhaus, M. Shellenberger, Energy Emergence: Rebound and Backfire as Emergent Phenomena (Breakthrough Institute, 2011)Google Scholar
  9. G. Katsaros, P. Stichler, J. Subirats, J. Guitart, Estimation and forecasting of ecological efficiency of virtual machines. Future Gener. Comput. Syst. 55, 480–494 (2016)CrossRefGoogle Scholar
  10. R.G. Lipsey, K.I. Carlaw, C.T. Bekar, Economic Transformations: General Purpose Technologies and Long-Term Economic Growth: General Purpose Technologies and Long-Term Economic Growth (Oxford University Press, 2005)Google Scholar
  11. J. Malmodin, P. Bergmark, Exploring the effect of ICT solutions on GHG emissions in 2030. Third international conference on ICT for sustainability (ICT4S 2015) (Atlantis Press, 2015), pp. 37–46Google Scholar
  12. V.M. Mantas, Z. Liu, A.J.S.C. Pereira, A web service and android application for the distribution of rainfall estimates and earth observation data. Comput. Geosci. 77, 66–76 (2015)CrossRefGoogle Scholar
  13. P. Mell, T. Grance, The NIST definition of cloud computing. National Inst. Stand. Technol. 53, 50 (2011)Google Scholar
  14. E. Oró, V. Depoorter, N. Pflugradt et al., Overview of direct air free cooling and thermal energy storage potential energy savings in data centres. Appl. Therm. Eng. 85, 100–110 (2015)CrossRefGoogle Scholar
  15. I. Oscarsson, A forecast of the Cloud. Master thesis. Lund University, Sweden, 2014Google Scholar
  16. R. Ranjan, B. Benatallah, S. Dustdar et al., Cloud resource orchestration programming: overview, issues, and directions. IEEE Internet Comput. 19, 46–56 (2015)CrossRefGoogle Scholar
  17. M. Sedlacko, A. Martinuzzi, K. Dobernig, A Systems Thinking View on Cloud Computing and Energy Consumption, ICT for Sustainability 2014 (ICT4S-14) (Atlantis Press, 2014a)Google Scholar
  18. M. Sedlacko, A. Martinuzzi, I. Røpke et al., Participatory systems mapping for sustainable consumption: Discussion of a method promoting systemic insights. Ecol. Econ. 106, 33–43 (2014b)Google Scholar
  19. J. Shamsi, M.A. Khojaye, M.A. Qasmi, Data-intensive cloud computing: requirements, expectations, challenges, and solutions. J Grid computing 11, 281–310 (2013)CrossRefGoogle Scholar
  20. S. Sorrell, The Rebound Effect: An Assessment of the Evidence for Economy-Wide Energy Savings from Improved Energy Efficiency (UK Energy Research Centre, London, 2007)Google Scholar
  21. S. Subashini, V. Kavitha, A survey on security issues in service delivery models of cloud computing. J. Network Comput. Appl. 34, 1–11 (2011)CrossRefGoogle Scholar
  22. N. Terry, J. Palmer, Trends in home computing and energy demand. Build. Res. Inf. (ahead-of-print) 1–13 (2015)Google Scholar
  23. L. Velasco, A. Asensio, J. Berral et al., Towards a carrier SDN: an example for elastic inter-datacentre connectivity. Opt. Express 22, 55–61 (2014)CrossRefGoogle Scholar
  24. N. Verma, R. Kumar, A method for improving data delivery efficiency in vehicular adhoc networks. Int. J. Adv. Sci. Technol. 44, 11–24 (2012)Google Scholar
  25. H.J. Walnum, C. Aall, S. Løkke, Can rebound effects explain why sustainable mobility has not been achieved? Sustainability 6, 9510–9537 (2014)CrossRefGoogle Scholar
  26. M. Walterbusch, B. Martens, F. Teuteberg, Evaluating cloud computing services from a total cost of ownership perspective. Manage. Res. Rev. 36, 613–638 (2013)CrossRefGoogle Scholar
  27. J. Whiteaker, F. Schneider, R. Teixeira et al., Expanding home services with advanced gateways. ACM SIGCOMM Comput. Comm. Rev. 42, 37–43 (2012)CrossRefGoogle Scholar
  28. D.R. Williams, P. Thomond, I. Mackenzie, The greenhouse gas abatement potential of enterprise cloud computing. Environ. Model Softw. 56, 6–12 (2014)CrossRefGoogle Scholar
  29. H. Zhang, S. Shao, H. Xu, H. Zou, C. Tian, Free cooling of data centers: a review. Renew. Sustain. Ener. Rev. 35, 171–182 (2014)CrossRefGoogle Scholar
  30. S. Zhang, J. Yang, Y. Shi, Dynamic energy storage control for reducing electricity cost in data centres. Math. Probl. Eng. (2015)Google Scholar
  31. P. Yue, H. Zhou, J. Gong, L. Hu, Geoprocessing in cloud computing platforms–a comparative analysis. Int. J. Digital Earth 6, 404–425 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Western Norway Research InstituteSogndalNorway
  2. 2.Huawei TechnologiesStockholmSweden

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