The Design of Personal Virtualization Rule Based on Context-Awareness in Environment of Cloud Computing

  • Hyogun Yoon
  • Hanku Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6935)


Cloud services are possible to consisting of personal service using service virtualization for user. However, this process set up a group of users, and has offers a group service of common structure by organized group than a service of personalized cloud. Therefore, this paper proposes a rule of virtualization to provide personalized service with optimal resources in cloud computing. Proposed rule constitute personalized service to fit the user’s status by analyzing user’s situation. A model for personalized service configuration is based on MLP(Multi-Layer Perceptron). And, it should ensure the connectivity of service using connection weights for link of each layer. A history of Matched service with served DR(Direct Relationship) reconstruct the user’s context information by feedback. Thus, proposed rule provides personalized service automatically configured the information and application on user’s situation.


Cloud Computing Personal Virtualization Context-awareness MLP(Multi-Layer Perceptron) Service 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldberg, R.P.: Survey of Virtual Machine Research. IEEE Computer Magazine, 34–45 (1974)Google Scholar
  2. 2.
    Wang, S.X., Zhang, L., Wang, S., et al.: A cloud-based trust model for evaluating quality of Web services. Journal of Computer Science and Technology 25(6), 1130–1142 (2010)CrossRefGoogle Scholar
  3. 3.
    Tchepnda, C., Riguidel, M.: Distributed Trust Infrastructure and Trust-Security articulation:Application to Heterogeneous networks. In: Advanced Information Networking and Applications (AINA 2006), vol. 2, pp. 33–38 (2006)Google Scholar
  4. 4.
    Brakensiek, J., Droge, A., Hartig, H., Lackorzynski, A., Botteck, M.: Virtualization as an Enabler for Security in Mobile Devices. In: Proc. of the 1st Workshop on Isolation and Integration in Embedded Systems, pp. 17–22 (2008)Google Scholar
  5. 5.
    Hypponen, M.: Malware Goes Mobile. Scientific American 295(5), 70–77 (2006)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Zhang, X., Kunjithapatham, A., Jeong, S., Simon, G.: Towards an Elastic Application Model for Augmenting the Computing Capabilities of Mobile Devices with Cloud Computing. Mobile Networks and Applicatons 16(3), 270–284 (2011)CrossRefGoogle Scholar
  8. 8.
    Mui, L.: Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks. PhD Thesis, Massachusetts Institute of Technology (2002)Google Scholar
  9. 9.
    Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electronic Commerce Research and Applications 8(1), 37–47 (2009)CrossRefGoogle Scholar
  10. 10.
    Zhang, T., Du, Z., Chen, Y., Ji, X., Wang, X.: Typical Virtual Appliances: An optimized mechanism for virtual appliances provisioning and management. The Journal of Systems and Software 84(3), 377–387 (2011)CrossRefGoogle Scholar
  11. 11.
    Hwang, J., Suh, S., Heo, S., Park, C., Ryu, J., Park, S., Kim, C.: Xen on ARM:System Virtualization using Xen Hypervisor for ARM-based Secure Mobile Phones. In: Consumer Communications and Networking Conference 2008 (CCNC 2008), pp. 257–261 (2008)Google Scholar
  12. 12.
    Grundig Mobile U900,, Heiser, G.: Virtualization for Embedded Systems. Open Kernel Labs (2007)
  13. 13.
    Heiser, G.: The Motorola Evoke QA4-A Case Study in Mobile Virtualization. Open Kernel Labs (2009)Google Scholar
  14. 14.
    Ryu, E., Kim, I., Kim, J., Eom, Y.: MyAV:An All round virtual Machine Monitor for Mobile Environments. In: Proc. of the 8th IEEE International Conference on Industrial Informatics 2010 (INDIN 2010), pp. 657–662 (2010)Google Scholar
  15. 15.
    VMware MVP (Mobile Virtualization Platform),
  16. 16.
    Yoon, H., Lee, M., Gatton, T.M.: A multi-agent based user context Bayesian neural network analysis system. Artificial Intelligence Review 34(3), 261–270 (2010)CrossRefGoogle Scholar
  17. 17.
    Yoon, H., Kim, E., Lee, M., Lee, J., Gatton, T.M.: A Model of Sharing Based Multi-Agent to Support Adaptive Service in Ubiquitous Environment. In: Proceedings of the 2008 International Conference on Information Security and Assurance (ISA 2008), pp. 332–337 (2008)Google Scholar
  18. 18.
    Dey, A.K., Abowd, G.D.: Towards a Better Understanding of Context and Context-Awareness. In: Proceedings of the CHI 2000 Workshop on The What, Who, Where, When and How of Context-Awareness (2000)Google Scholar
  19. 19.
    Schilit, B.N., Adams, N.I., Want, R.: Context-Aware Computing Applications. In: IEEE Workshop on Mobile Computing Systems and Applications, pp. 85–90 (1994)Google Scholar
  20. 20.
    Crowley, J.L., Coutaz, J., Rey, G., Reignier, P.: Perceptual Components for Context Aware Computing. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 117–134. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Pascoe, J.: Adding generic contextual capabilities to wearable computers. In: Proceedings of 2nd International Symposium on Wearable Computers, pp. 92–99 (1998)Google Scholar
  22. 22.
    Moran, T.P., Dourish, P.: Introduction to This Special Issue on Comtext-Aware Computing. Human-Computer Ineraction (HCI) 16 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hyogun Yoon
    • 1
  • Hanku Lee
    • 1
  1. 1.Konkuk UniversitySeoulKorea

Personalised recommendations