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Generic context adaptation for mobile cloud computing environments

  • Gabriel OrsiniEmail author
  • Dirk Bade
  • Winfried Lamersdorf
Original Research

Abstract

Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, bandwidth, energy) to even allow re-source-demanding services like speech- or face recognition to be executed locally on a device. But compared to their stationary counterparts, mobile devices remain comparatively limited in terms of resources. Because of this, current approaches aim at extending mobile device capabilities with computation and storage resources offered by cloud services or other nearby devices. This paradigm, known as mobile cloud computing (MCC), is challenged by the dynamically changing context of mobile devices, which developers are required to take into account to decide, e.g., which application parts are when to offload. To rise to such and similar challenges we introduce the concept of Generic Context Adaptation (GCA), a data mining process that facilitates the adaptation of (mobile) applications to their current and future context. Moreover, we evaluate our approach with real usage data provided by the Nokia Mobile Data Challenge (MDC) as well as with CloudAware, a context-adaptive mobile middleware for MCC that supports automated and context-aware self-adaptation techniques.

Keywords

Mobile cloud computing Mobile edge computing Context adaptation Context awareness 

Notes

Acknowledgements

Parts of the research in this paper used the MDC Database made available by Idiap Research Institute, Switzerland and owned by Nokia.

References

  1. AlShahwan F, Faisal M, Ansa G (2016) Security framework for restful mobile cloud computing web services. J Ambient Intell Hum Comput 7(5):649–659CrossRefGoogle Scholar
  2. Chetan S, Al-Muhtadi J, Campbell R, Mickunas MD (2005) Mobile gaia: a middleware for ad-hoc pervasive computing. In: Consumer Communications and Networking Conference, 2005. CCNC. 2005 Second IEEE, IEEE, pp 223–228Google Scholar
  3. Chun BG, Ihm S, Maniatis P, Naik M, Patti A (2011) CloneCloud: elastic execution between mobile device and cloud. In: Proceedings of the 6. European Conference on Computer Systems, pp 301–314Google Scholar
  4. Cuervo E, Balasubramanian A, Cho Dk, Wolman A, Saroiu S, Chandra R, Bahl P (2010) Maui: making smartphones last longer with code offload. In: Proceedings of the 8th international conference on Mobile systems, applications, and services, ACM, pp 49–62Google Scholar
  5. Davis A, Parikh J, Weihl WE (2004) Edgecomputing: extending enterprise applications to the edge of the internet. In: Proceedings of the 13th international World Wide Web conference on Alternate track papers & postersGoogle Scholar
  6. Dey AK, Abowd GD (1999) Towards a better understanding of context and context-awareness. HUC ’99: Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing. Springer-Verlag, London, pp 304–307Google Scholar
  7. Dinh HT, Lee C, Niyato D, Wang P (2011) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611CrossRefGoogle Scholar
  8. Geihs K (2008) Selbst-adaptive software. Informatik-Spektrum 31(2):133–145CrossRefGoogle Scholar
  9. Gu T, Pung HK, Zhang DQ (2004) A middleware for building context-aware mobile services. In: Vehicular Technology Conference, 2004. VTC 2004-Spring. 2004 IEEE 59th, IEEE, vol 5Google Scholar
  10. Henricksen K, Indulska J, McFadden T, Balasubramaniam S (2005) Middleware for distributed context-aware systems. In: On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE, Springer, pp 846–863Google Scholar
  11. Kosta S, Aucinas A, Hui P, Mortier R, Zhang X (2012) Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: INFOCOM, 2012 Proceedings IEEE, IEEE, pp 945–953Google Scholar
  12. Laurila JK, Gatica-Perez D, Aad I, Blom J, Bornet O, Do Trinh Minh, Tri Dousse O, Eberle J, Miettinen M (2013) From big smartphone data to worldwide research: the mobile data challenge. Pervasive Mob Comput 9(6):752–771CrossRefGoogle Scholar
  13. Lim BY, Dey AK (2010) Toolkit to support intelligibility in context-aware applications. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, ACM, pp 13–22Google Scholar
  14. Mikalsen M, Paspallis N, Floch J, Stav E, Papadopoulos GA, Chimaris A (2006) Distributed context management in a mobility and adaptation enabling middleware (madam). In: Proceedings of the 2006 ACM symposium on Applied computing, ACM, pp 733–734Google Scholar
  15. Orsini G, Bade D, Lamerdorf W (2015) Computing at the mobile edge: Designing elastic android applications for computation offloading. In: 8th Joint IFIP Wireless and Mobile Networking Conference (WMNC), IEEE Explore Washington/DC, USA, p 8Google Scholar
  16. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454CrossRefGoogle Scholar
  17. Preuveneers D, Berbers Y (2007) Towards context-aware and resource-driven self-adaptation for mobile handheld applications. In: Proceedings of the 2007 ACM symposium on Applied computing, ACMGoogle Scholar
  18. Rouvoy R, Barone P, Ding Y, Eliassen F, Hallsteinsen S, Lorenzo J, Mamelli A, Scholz U (2009) Music: Middleware support for self-adaptation in ubiquitous and service-oriented environments. In: Software engineering for self-adaptive systems, SpringerGoogle Scholar
  19. Salber D, Dey AK, Abowd GD (1999) The context toolkit: aiding the development of context-enabled applications. In: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, CHI ’99, pp 434–441Google Scholar
  20. Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8(4):10–17CrossRefGoogle Scholar
  21. Wei EJ, Chan AT (2013) Campus: a middleware for automated context-aware adaptation decision making at run time. Pervasive Mob Comput 9(1):35–56CrossRefGoogle Scholar
  22. Xue Y, Deters R (2016) Towards horizontally scalable apps. J Ambient Intell Hum Comput 7(4):465–473CrossRefGoogle Scholar
  23. Yuan B, Herbert J, Emamian Y (2014) Smartphone-based activity recognition using hybrid classifier. In: Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication SystemsGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Gabriel Orsini
    • 1
    Email author
  • Dirk Bade
    • 1
  • Winfried Lamersdorf
    • 1
  1. 1.Distributed Systems Group, Department of Computer ScienceUniversity of HamburgHamburgGermany

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