A Framework for Seamless Execution of Mobile Applications in the Cloud

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 126)

Abstract

Limited resources of battery-operated mobile devices are a major obstacle for mobile applications. An obvious solution to this limitation is to leverage cloud computing, which offers virtually infinite resources on demand through the virtualization of physically distributed computing resources. A mobile device could offload a resource-intensive application to the cloud and support thin client interaction with the application over the Internet. As such, cloud computing enhances the computing capability of mobile devices, as well as saving energy of mobile devices. In this paper, therefore, we propose a framework supporting the seamless execution of mobile applications on the cloud. In particular, the novel aspect of our approach is that a mobile cloud application, itself, is treated as data, so it can be replicated within the cloud, thus being able to reduce both latency and energy consumption of the communication. This paper is a work-in-progress report of our research.

Keywords

Mobile Device Cloud Computing Virtual Machine Mobile Application Storage Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Caceres, R., et al.: Reincarnating PCs with Portable Soul-Pads. In: Proceedings of 3rd International Conference on Mobile Systems, Applications, and Services (2005)Google Scholar
  2. 2.
    Chen, G., et al.: Studying Energy Trade Offs in Offloading Computation/Compilation in Java-Enabled Mobile Devices. IEEE Transactions on Parallel and Distributed Systems 15(9), 795–809 (2005)CrossRefGoogle Scholar
  3. 3.
    Clark, C., et al.: Live Migration of Virtual Machines. In: Proceedings of 2nd Usenix Symposiums on Networked Systems Design and Implementation (2005)Google Scholar
  4. 4.
    Creeger, M.: CTO Roundtable: Cloud Computing. ACM Queue, 1–2 (2009)Google Scholar
  5. 5.
    Gu, X., et al.: Adaptive Offloading for Pervasive Computing. IEEE Pervasive Computing 3(3), 66–73 (2004)CrossRefGoogle Scholar
  6. 6.
    Kozuch, M., Satyanarayanan, M.: Internet Suspend/Resume. In: Proceedings of 4th IEEE Workshop on Mobile Computing Systems and Applications (2002)Google Scholar
  7. 7.
    Kumar, K., Lu, Y.: Cloud Computing for Mobile Users: Can Offloading Computation Save Energy? IEEE Computer 43(4), 51–56 (2010)CrossRefGoogle Scholar
  8. 8.
    Miettinen, A., Nurminen, J.: Energy Efficiency of Mobile Clients in Cloud Computing. In: Proceedings of 2nd USENIX Workshop on Hot Topics in Cloud Computing, Boston, MA (June 2010)Google Scholar
  9. 9.
    Mobile Application Stores State of Play, Distimo, Mobile World Congress (2010)Google Scholar
  10. 10.
    Satyanarayanan, M., et al.: Pervasive Personal Computing is an Internet Suspend/Resume System. IEEE Internet Computing 11(2), 16–25 (2007)CrossRefGoogle Scholar
  11. 11.
    Satyanarayanan, M., et al.: The case for VM-based Cloudlets in Mobile Computing. IEEE Pervasive Computing 8(4), 14–23 (2009)CrossRefGoogle Scholar
  12. 12.
    Walker, E., Brisken, W., Romney, J.: To Lease or Not To Lease from Storage Clouds. IEEE Computer 43(4), 44–50 (2010)CrossRefGoogle Scholar
  13. 13.
    Yang, K., Ou, S., Chen, H.: On Effective Offloading Services for Resource-Constrained Mobile Devices Running Heavier Mobile Internet Applications. IEEE Communications Magazine 46(1), 56–63 (2008)CrossRefGoogle Scholar
  14. 14.
    Walker, E., Brisken, W., Romney, J.: To Lease or Not to Lease from Storage Clouds. IEEE Computer 43(4), 44–50 (2010)CrossRefGoogle Scholar
  15. 15.
    Wang, C., Li, Z.: Parametric Analysis for Adaptive Computation Offloading. ACM SIGPLAN Notices 39(6), 119–130 (2004)CrossRefGoogle Scholar
  16. 16.
    Wang, C., Li, Z.: A Computation Offloading Scheme on Handheld Devices. Journal of Parallel and Distributed Computing 64(6), 740–746 (2004)MATHCrossRefGoogle Scholar
  17. 17.
    Web Services Architecture, http://www.w3.org/TR/ws-arch
  18. 18.
    Wolski, et al.: Using Bandwidth Data to Make Computation Offloading Decisions. In: Proceedings of IEEE Internal Symposiums on Parallel and Distributed Processing, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceKyonggi UniversitySuwonKorea

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