An Analysis of Power Consumption in Mobile Cloud Computing

  • Abdelmounaam RezguiEmail author
  • Zaki Malik
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 581)


With the rapid proliferation of mobile devices, mobile cloud computing is emerging as an increasingly omnipresent paradigm enabling users to use battery-powered mobile devices to access a wide range of compute-intensive applications hosted on the clouds. Often, the assumption is that mobile devices consume less power when they access an application run on the cloud than when the application is run on the device itself. This, however, is increasingly questionable with the significant recent progress in improving power efficiency of mobile devices (e.g., using ultra low power GPUs). This paper aims at analyzing and comparing the benefits of these two alternatives using mobile cloud gaming as an example. Our evaluation shows that, despite the recent advances towards reducing power consumption in mobile devices, mobile cloud computing remains the best of the two alternatives in a wide range of scenarios.


Mobile cloud gaming GPUs NICs Power consumption Visualization as a Service (VaaS) Offloading 


  1. 1.
    Elijah: Cloudlet-based Mobile Computing.
  2. 2.
    Carroll, A., Heiser, G.: An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, USENIXATC 2010, pp. 21–21. USENIX Association, Berkeley (2010).
  3. 3.
    Ellouze, A., Gagnaire, M., Haddad, A.: A mobile application offloading algorithm for mobile cloud computing. In: 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), pp. 34–40, March 2015Google Scholar
  4. 4.
    Ericsson: Ericsson mobility report: on the pulse of the networked society. Technical report, Ericsson, June 2015Google Scholar
  5. 5.
    GFXBench: Gfxbench 3.0 directx (2015).
  6. 6.
    Halperin, D., Greenstein, B., Sheth, A., Wetherall, D.: Demystifying 802.11n power consumption. In: Proceedings of the International Conference on Power-Aware Computing and Systems. HotPower, Vancouver (2010)Google Scholar
  7. 7.
    Hao, S., Li, D., Halfond, W.G.J., Govindan, R.: Estimating mobile application energy consumption using program analysis. In: Proceedings of the the International Conference on Software Engineering (ICSE), San Francisco, California, May 2013Google Scholar
  8. 8.
    Hewlett Packard: HP EliteBook Folio 1040 G1 Notebook PC. Technical report (2013)Google Scholar
  9. 9.
    Hruska, J.: Nvidia’s Tegra 4 Demystified: 28nm, 72-core GPU, Integrated LTE, and Questionable Power Consumption (2013).
  10. 10.
    Kim, Y.G., Kim, M., et al.: A novel GPU power model for accurate smartphone power breakdown. ETRI J. 37(1), 157–164 (2015)CrossRefGoogle Scholar
  11. 11.
    Kumar, K., Liu, J., Lu, Y.H., Bhargava, B.: A survey of computation offloading for mobile systems. Mob. Netw. Appl. 18(1), 129–140 (2013)CrossRefGoogle Scholar
  12. 12.
    Lampe, U., Hans, R., Steinmetz, R.: Will mobile cloud gaming work? findings on latency, energy, and cost. In: Proceedings of the 2013 IEEE Second International Conference on Mobile Services, MS 2013, pp. 103–104. IEEE Computer Society, Washington (2013).
  13. 13.
    Lee, K., Chu, D., Cuervo, E., Kopf, J., Grizan, S., Wolman, A., Flinn, J.: DeLorean: using speculation to enable low-latency continuous interaction for mobile cloud gaming. Technical report, Microsoft Research, August 2014Google Scholar
  14. 14.
    Li, B., Pei, Y., Wu, H., Shen, B.: Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds. J. Supercomput. 71(8), 3009–3036 (2015)CrossRefGoogle Scholar
  15. 15.
    Madden, J.: MIMO adoption in mobile communications forecast: devices by operating system and user type, worldwide, 2010–2017, 1Q13 Update. Technical report, Mobile Experts, June 2011Google Scholar
  16. 16.
    Magurawalage, C.M.S., Yang, K., Hu, L., Zhang, J.: Energy-efficient and network-aware offloading algorithm for mobile cloud computing. Comput. Netw. 74, 22–33 (2014)CrossRefGoogle Scholar
  17. 17.
    MarketsandMarkets: World Mobile Applications Market - Advanced Technologies, Global Forecast (2010–2015). Technical report, MarketsandMarkets (2010)Google Scholar
  18. 18.
    Milanesi, C., Tay, L., Cozza, R., Atwal, R., Nguyen, T.H., Tsai, T., Zimmermann, A., Lu, C.K.: Forecast: devices by operating system and user type, worldwide, 2010–2017, 1Q13 Update. Technical report, Gartner, 28 March 2013Google Scholar
  19. 19.
    Netgear: Next Generation Gigabit WiFi - 802.11ac. Technical report (2012)Google Scholar
  20. 20.
  21. 21.
    NoteBookCheck: Computer Games on Laptop Graphic Cards (2014).
  22. 22.
    Nvidia: Building Cloud Gaming Servers (2015).
  23. 23.
  24. 24.
  25. 25.
    ReportLinker: Global Mobile Application Market 2015–2019. Technical report, ReportLinker, March 2015Google Scholar
  26. 26.
    Saha, S.K., Deshpande, P., Inamdar, P.P., Sheshadri, R.K., Koutsonikolas, D.: Power-throughput tradeoffs of 802.11n/ac in smartphones. In: Proceedings of the 34th IEEE International Conference on Computer Communications (INFOCOM), Hong Long, Spain, 26 April–1 May 2015Google Scholar
  27. 27.
    Satyanarayanan, M., Chen, Z., Ha, K., Hu, W., Richter, W., Pillai, P.: Cloudlets: at the leading edge of mobile-cloud convergence. In: 2014 6th International Conference on Mobile Computing, Applications and Services (MobiCASE), pp. 1–9, November 2014Google Scholar
  28. 28.
    Shiraz, M., Gani, A., Khokhar, R., Buyya, R.: A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Commun. Surv. Tutorials 15(3), 1294–1313 (2013)CrossRefGoogle Scholar
  29. 29.
    Soliman, O., Rezgui, A., Soliman, H., Manea, N.: Mobile cloud gaming: issues and challenges. In: Daniel, F., Papadopoulos, G.A., Thiran, P. (eds.) MobiWIS 2013. LNCS, vol. 8093, pp. 121–128. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  30. 30.
    Thompson, C., Schmidt, D.C., Turner, H.A., White, J.: Analyzing mobile application software power consumption via model-driven engineering. In: Benavente-Peces, C., Filipe, J. (eds.) PECCS, pp. 101–113. SciTePress (2011)Google Scholar
  31. 31.
    Zeng, Y., Pathak, P.H., Mohapatra, P.: A first look at 802.11ac in action: energy efficiency and interference characterization. In: Proceedings of the 13th IFIP International Conferences on Networking, Trondheim, Norway, 2–4 June 2014Google Scholar
  32. 32.
    Zhou, B., Dastjerdi, A.V., Calheiros, R.N., Srirama, S.N., Buyya, R.: A context sensitive offloading scheme for mobile cloud computing service. In: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD), pp. 869–876, June 2015Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNew Mexico TechSocorroUSA
  2. 2.Department of Computer ScienceWayne State UniversityDetroitUSA

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