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Abstract

Although smartphones are increasingly becoming more and more powerful, enabling pervasiveness is severely hindered by the resource limitations of mobile devices. The combination of social interactions and mobile devices in the form of ‘crowd computing’ has the potential to surpass these limitations. In this paper, we introduce Honeybee; a crowd computing framework for mobile devices. Honeybee enables mobile devices to share work, utilize local resources and human collaboration in the mobile context. It employs ‘work stealing’ to effectively load balance tasks across nodes that are a priori unknown. We describe the design of Honeybee, and report initial experimental data from applications implemented using Honeybee.

Keywords

mobile crowd computing mobile cloud computing remote execution offloading crowd sourcing 

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References

  1. 1.
    Amazon mechanical turk, https://www.mturk.com/
  2. 2.
  3. 3.
    Afridi, A.H.: Mobile social computing: Swarm intelligence based collaboration. Lecture Notes in Engineering and Computer Science, vol. 2198 (2012)Google Scholar
  4. 4.
    Blumofe, R.D., Joerg, C.F., Kuszmaul, B.C., Leiserson, C.E., Randall, K.H., Zhou, Y.: Cilk: an efficient multithreaded runtime system. SIGPLAN Not. 30, 207–216 (1995)CrossRefGoogle Scholar
  5. 5.
    Blumofe, R.D., Leiserson, C.E.: Scheduling multithreaded computations by work stealing. J. ACM 46(5), 720–748 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chun, B.-G., Ihm, S., Maniatis, P., Naik, M., Patti, A.: Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the Sixth Conference on Computer Systems, EuroSys 2011, pp. 301–314. ACM, New York (2011)Google Scholar
  7. 7.
    Doolan, D.C., Tabirca, S., Yang, L.T.: Mmpi a message passing interface for the mobile environment. In: Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2008, pp. 317–321. ACM, New York (2008)Google Scholar
  8. 8.
    Fernando, N., Loke, S.W., Rahayu, W.: Dynamic mobile cloud computing: Ad hoc and opportunistic job sharing. In: 2011 Fourth IEEE International Conference on Utility and Cloud Computing (UCC), pp. 281–286 (December 2011)Google Scholar
  9. 9.
    Fernando, N., Loke, S.W., Rahayu, W.: Mobile crowd computing with work stealing. In: Proceedings of the 15th International Workshop on Mobile Cloud Computing Technologies and Applications (in NBiS) (September 2012)Google Scholar
  10. 10.
    Fernando, N., Loke, S.W., Rahayu, W.: Mobile cloud computing: A survey. Future Generation Computer Systems 29(1), 84–106 (2013)CrossRefGoogle Scholar
  11. 11.
    Franklin, M.J., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: Crowddb: answering queries with crowdsourcing. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, SIGMOD 2011, pp. 61–72. ACM, New York (2011)Google Scholar
  12. 12.
    Howe, J.: The rise of crowdsourcing (2006), http://www.wired.com/wired/archive/14.06/crowds.html
  13. 13.
    Huerta-Canepa, G., Lee, D.: A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond, MCS 2010, pp. 6:1–6:5. ACM, New York (2010)Google Scholar
  14. 14.
    Jovanovic, N., Bender, M.A.: Task scheduling in distributed systems by work stealing and mugging - a simulation study. In: Proceedings of the 24th International Conference on Information Technology Interfaces, ITI 2002, vol. 1, pp. 259–264 (2002)Google Scholar
  15. 15.
    Lu, W., Gannon, D.: Parallel xml processing by work stealing. In: Proceedings of the 2007 Workshop on Service-Oriented Computing Performance: Aspects, Issues, and Approaches, SOCP 2007, pp. 31–38. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Marinelli, E.E.: Hyrax: Cloud Computing on Mobile Devices using MapReduce. Carnegie Mellon University, Masters thesis (2009)Google Scholar
  17. 17.
    Murray, D.G., Yoneki, E., Crowcroft, J., Hand, S.: The case for crowd computing. In: Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds, MobiHeld 2010, pp. 39–44. ACM, New York (2010)CrossRefGoogle Scholar
  18. 18.
    Ra, M.-R., Liu, B., Porta, T.F.L., Govindan, R.: Medusa: a programming framework for crowd-sensing applications. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 337–350. ACM, New York (2012)Google Scholar
  19. 19.
    Satyanarayanan, M.: Fundamental challenges in mobile computing. In: Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing PODC 1996, pp. 1–7. ACM, New York (1996)CrossRefGoogle Scholar
  20. 20.
    Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing 8(4), 14–23 (2009)CrossRefGoogle Scholar
  21. 21.
    Yan, T., Kumar, V., Ganesan, D.: CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys 2010, pp. 77–90. ACM, New York (2010)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Niroshinie Fernando
    • 1
  • Seng W. Loke
    • 1
  • Wenny Rahayu
    • 1
  1. 1.Department of Computer Science and Computer EngineeringLa Trobe UniversityAustralia

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