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Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment

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Abstract

Mobile cloud computing (MCC) is an emerging technology to relieve the tension between compute-intensive mobile applications and resource-constrained mobile terminals by offloading computing tasks to remote cloud servers. In this paper, we consider a novel MCC architecture consisting of remote cloud server, cloudlet and mobile terminal to guarantee low latency and low energy mobile consumption. To overcome the main bottlenecks of wireless bandwidth between mobile terminal and cloudlet, and the computation capability of cloudlet, the joint optimization strategy is proposed to enhance the quality of mobile cloud service. We formulate the wireless bandwidth and computing resource allocation model as a triple-stage Stackelberg game, and solve it by using backward method. In addition, the interplays of triple-stage game are discussed and the subgame optimal equilibrium for each stage is analyzed. An iterative algorithm is proposed to obtain Stackelberg equilibrium. Numerical results demonstrate the effectiveness of the proposed algorithm.

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References

  1. Cox P (2011) Mobile cloud computing: devices, trends, issues, and the enabling technologies. IBM developer Works, 1–10

  2. Song W, Su X (2011) Review of mobile cloud computing Proceedings of IEEE 3rd. International Conference on Communication Software and Networks (ICCSN), pp 1–4. doi:10.1109/ICCSN.2011.6014374

  3. Satyanarayanan M, Lewis G, Morris E, Simanta S, Boleng J, Ha K (2013) The role of cloudlets in hostile environments. IEEE Pervasive Comput 12(4):40–49. doi:10.1109/MPRV.2013.77

    Article  Google Scholar 

  4. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23. doi:10.1109/MPRV.2009.82

    Article  Google Scholar 

  5. Liu Y, Lee M, Zheng Y (2015) Adaptive multi-resource allocation for cloudlet-based mobile cloud computing system. IEEE Trans Mob Comput PP(99):1. doi:10.1109/TMC.2015.2504091

  6. Wang Y, Meng S, Chen Y, Sun R, Wang X, Sun K (2016) Multi-leader multi-follower Stackelberg game based dynamic resource allocation for mobile cloud computing environment. Wireless Personal Communications, 1–20. doi:10.1007/s11277-016-3351-4

  7. Misra S, Das S, Khatua M, Obaidat M (2014) Qos-guaranteed bandwidth shifting and redistribution in mobile cloud environment. IEEE Trans Cloud Comput 2(2):181–193. doi:10.1109/TCC.2013.19

    Article  Google Scholar 

  8. Kaewpuang R, Niyato D, Wang P, Hossain E (2013) A framework for cooperative resource management in mobile cloud computing. IEEE J Sel Areas Commun 31(12):2685–2700. doi:10.1109/JSAC.2013.131209

    Article  Google Scholar 

  9. Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(4):974–983. doi:10.1109/TPDS.2014.2316834

    Article  Google Scholar 

  10. Di Lorenzo P, Barbarossa S, Sardellitti S (2013) Joint optimization of radio resources and code partitioning in mobile cloud computing. arXiv:1307.3835

  11. Yin Z, Yu FR, Bu S, Han Z (2015) Joint cloud and wireless networks operations in mobile cloud computing environments with telecom operator cloud. IEEE Trans Wirel Commun 14(7):4020–4033

    Article  Google Scholar 

  12. Tang L, Chen H (2014) Joint pricing and capacity planning in the iaas cloud market. IEEE Transactions on Cloud Computing. doi:10.1109/TCC.2014.2372811

  13. Wang S, Dey S (2010) Rendering adaptation to address communication and computation constraints in cloud mobile gaming Proceedings of IEEE Global Telecommunications Conference (GLOBECOM 2010). doi:10.1109/GLOCOM.2010.5684144, pp 1–6

  14. Wang Y, Lin X, Pedram M (2013) A nested two stage game-based optimization framework in mobile cloud computing system Processings of IEEE 7th international symposium on service oriented system engineering (SOSE), pp 494–502. doi:10.1109/SOSE.2013.68

  15. Vakilinia S, Qiu D, Ali MM (2014) Optimal multi-dimensional dynamic resource allocation in mobile cloud computing. EURASIP J Wirel Commun Netw 1(1):1–14. doi:10.1186/1687-1499-2014-201

    Google Scholar 

  16. Addis B, Ardagna D, Panicucci B, Squillante MS, Zhang L (2013) A hierarchical approach for the resource management of very large cloud platforms. IEEE Trans Dependable Secure Comput 10(5):253–272. doi:10.1109/TDSC.2013.4

    Article  Google Scholar 

  17. Niyato D, Vasilakos AV, Kun Z (2011) Resource and revenue sharing with coalition formation of cloud providers: Game theoretic approach Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 215–224. doi:10.1109/CCGrid.2011.30

  18. Kotevska O, Lbath A, Bouzefrane S (2016) Toward a real-time framework in cloudlet-based architecture. Tsinghua Sci Technol 21(1):80–88. doi:10.1109/TS-T.2016.7399285

    Article  Google Scholar 

  19. Powers N, et al. (2015) The cloudlet accelerator: Bringing mobile-cloud face recognition into real-time Proceedings of IEEE Globecom Workshops (GC Wkshps), pp 1–7. doi:10.1109/GLOCOMW.2015.7414055

  20. Goldsmith A (2005) Wireless communication. Cambridge University, Cambridge

    Book  Google Scholar 

  21. Fudenberg D, Tirole J (1991) Game theory. Cambridge, Massachusetts, 393

Download references

Acknowledgements

This work is supported by National 863 Project (2015AA015701), National Nature Science Foundation of China (61372113, 61421061) and Natural Science Foundation of Inner Mongolia (2014MS0602, 2015MS0602).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching, Beijing University of Post and Telecommunications, Beijing, China

    Sachula Meng, Ying Wang & Zhongyu Miao

  2. College of Electronic and Information Engineering, Inner Mongolia University, Hohhot, China

    Kai Sun

Authors
  1. Sachula Meng
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  2. Ying Wang
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  3. Zhongyu Miao
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  4. Kai Sun
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Corresponding author

Correspondence to Ying Wang.

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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, 462–472 (2018). https://doi.org/10.1007/s12083-017-0544-x

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