POEM: Pricing Longer for Edge Computing in the Device Cloud

  • Qiankun Yu
  • Jigang WuEmail author
  • Long Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)


Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average.


Edge computing Computation offloading Multiple rounds Mobile device cloud Long-term Auction 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 61702115 and 61672171, Natural Science Foundation of Guangdong, China under Grant No. 2018B030311007, and Major R&D Project of Educational Commission of Guangdong under Grant No. 2016KZDXM052. This work was also supported by China Postdoctoral Science Foundation Fund under Grant No. 2017M622632.


  1. 1.
    Miettinen, A.P., Nurminen, J.K.: Energy efficiency of mobile clients in cloud computing. In: Usenix Conference on Hot Topics in Cloud Computing, Boston, MA, p. 4. USENIX Association (2010)Google Scholar
  2. 2.
    Burgstahler, D., Richerzhagen, N., Englert, F., et al.: Switching push and pull: an energy efficient notification approach. In: Anchorage, AK, USA, pp. 68–75. IEEE (2014)Google Scholar
  3. 3.
    Ahn, S., Lee, J., et al.: Competitive partial computation offloading for maximizing energy efficiency in mobile cloud computing. IEEE Access 6, 899–912 (2018)CrossRefGoogle Scholar
  4. 4.
    Thanapal, P., Durai, M.A.S.: A framework for computational offloading to extend the energy of mobile devices in mobile cloud computing. Int. J. Embed. Syst. 9(5), 444 (2017)CrossRefGoogle Scholar
  5. 5.
    Chun, B.G., Ihm, S., Maniatis, P., et al.: CloneCloud: elastic execution between mobile device and cloud. In: Salzburg, Austria, pp. 301–314. ACM (2011)Google Scholar
  6. 6.
    Khan, A.U.R., Othman, M., et al.: A survey of mobile cloud computing application models. IEEE Commun. Surv. Tutor. 16(1), 393–413 (2014)CrossRefGoogle Scholar
  7. 7.
    Wu, J., Yuen, C., Cheung, N.M., et al.: Enabling adaptive high-frame-rate video streaming in mobile cloud gaming applications. IEEE Trans. Circuits Syst. Video Technol. 25(12), 1988–2001 (2015)CrossRefGoogle Scholar
  8. 8.
    Chunlin, L.I., Layuan, L.I.: An optimization approach for utilizing cloud services for mobile devices in cloud environment. Informatica 26(1), 89–110 (2015)CrossRefGoogle Scholar
  9. 9.
    Meng, S., Wang, Y., Miao, Z., et al.: Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment. Peer-to-Peer Netw. Appl. 11(3), 462–472 (2017)CrossRefGoogle Scholar
  10. 10.
    Tocz, K., Nadjmtehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. CoRR (2018)Google Scholar
  11. 11.
    Chen, L., Wu, J., Dai, H.N., et al.: BRAINS: joint bandwidth-relay allocation in multi-homing cooperative D2D networks. IEEE Trans. Veh. Technol. 99(99) (2018)Google Scholar
  12. 12.
    Chen, L., Wu, J., Zhang, X.X., et al.: TARCO: two-stage auction for D2D relay aided computation resource allocation in HetNet. IEEE Trans. Serv. Comput. PP(99), 1 (2017)Google Scholar
  13. 13.
    Mtibaa, A., Fahim, A., Harras, K.A., et al.: Towards resource sharing in mobile device clouds: power balancing across mobile devices. In: ACM SIGCOMM Workshop on Mobile Cloud Computing, Hong Kong, China, pp. 51–56. ACM (2013)Google Scholar
  14. 14.
    Mtibaa, A., Harras, K.A., Fahim, A.: Towards computational offloading in mobile device clouds. In: Bristol, UK, pp. 331–338. IEEE (2014)Google Scholar
  15. 15.
    Fahim, A., Mtibaa, A., Harras, K.A.: Making the case for computational offloading in mobile device clouds. In: Networking (ed.), pp. 203–205. ACM, New York (2013)Google Scholar
  16. 16.
    Habak, K., Shi, C., et al.: Elastic mobile device clouds: leveraging mobile devices to provide cloud computing services at the edge. In: Fog for 5G and IoT (2017)CrossRefGoogle Scholar
  17. 17.
    Miluzzo, E., Chen, Y.F.: Vision: mClouds - computing on clouds of mobile devices. In: ACM Workshop on Mobile Cloud Computing and Services, Low Wood Bay, Lake District, UK, pp. 9–14. ACM (2012)Google Scholar
  18. 18.
    Song, J., et al.: Energy-traffic tradeoff cooperative offloading for mobile cloud computing. In: Quality of Service, Hong Kong, China, pp. 284–289. IEEE (2014)Google Scholar
  19. 19.
    Wang, X., Chen, X., Wu, W., et al.: Cooperative application execution in mobile cloud computing: a Stackelberg Game approach. IEEE Commun. Lett. 20(5), 946–949 (2016)CrossRefGoogle Scholar
  20. 20.
    Zaman, S., Grosu, D.: Combinatorial auction-based allocation of virtual machine instances in clouds. In: IEEE Second International Conference on Cloud Computing Technology and Science. IEEE Computer Society, Indianapolis, IN, USA, pp. 127–134. IEEE (2010)Google Scholar
  21. 21.
    Shi, W., Zhang, L., Wu, C., et al.: An online auction framework for dynamic resource provisioning in cloud computing. IEEE/ACM Trans. Netw. 24(4), 2060–2073 (2016)CrossRefGoogle Scholar
  22. 22.
    Zhu, Y., Li, B., Li, Z.: Truthful spectrum auction design for secondary networks. In: IEEE INFOCOM, Orlando, FL, USA, pp. 873–881. IEEE (2012)Google Scholar
  23. 23.
    Wang, X., Huang, L., Xu, H., et al.: Social welfare maximization auction for secondary spectrum markets: a long-term perspective. In: IEEE International Conference on Sensing, Communication, and Networking, London, UK, pp. 1–9. IEEE (2016)Google Scholar
  24. 24.
    Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. J. Financ. 16(1), 8–37 (1961)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Li, H., Wu, C., Li, Z.: Socially-optimal online spectrum auctions for secondary wireless communication. In: Computer Communications, Kowloon, Hong Kong, pp. 2047–2055. IEEE (2015)Google Scholar
  26. 26.
    Jin, A., Song, W., Zhuang, W.: Auction-based resource allocation for sharing cloudlets in mobile cloud computing. IEEE Trans. Emerg. Top. Comput. 6(1), 45–57 (2018)CrossRefGoogle Scholar
  27. 27.
    Wang, X., Chen, X., Wu, W.: Towards truthful auction mechanisms for task assignment in mobile device clouds. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, Atlanta, GA, USA, pp. 1–9. IEEE (2017)Google Scholar
  28. 28.
    Wang, H., Guo, S., Cao, J., et al.: MELODY: a long-term dynamic quality-aware incentive mechanism for crowdsourcing. IEEE Trans. Parallel Distrib. Syst. 29(4), 901–914 (2018)CrossRefGoogle Scholar
  29. 29.
    Cherfi, N., Hifi, M.: A column generation method for the multiple-choice multi-dimensional knapsack problem. Comput. Optim. Appl. 46(1), 51–73 (2010)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Briest, P., Krysta, P., Vcking, B.: Approximation techniques for utilitarian mechanism design. In: Thirty-Seventh ACM Symposium on Theory of Computing, Baltimore, MD, USA, pp. 39–48. ACM (2016)Google Scholar
  31. 31.
    MATLAB Homepage. Accessed 4 June 2018

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Guangdong University of technologyGuangzhouChina

Personalised recommendations