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Task Offloading with Execution Cost Minimization in Heterogeneous Mobile Cloud Computing

  • Xing Liu
  • Songtao Guo
  • Yuanyuan Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)

Abstract

Mobile cloud computing (MCC) can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading remoteable tasks from resources-constrained SMDs onto the resource-rich cloud. However, it remains a challenge issue how to appropriately partition applications and select the suitable cloud to offload the task under the constraints of execution cost including completion time of the application and energy consumption of SMDs. To address such a challenge, in this paper, we first formulate the partitioning and cloud selection problem into execution cost minimization problem. To solve the optimization problem, we then propose a system framework for adaptive partitioning and dynamic selective offloading. Based on the framework, we design an optimal cloud selection algorithm with execution cost minimization which consists of offloading judgement and cloud selection. Finally, our experimental results in a real testbed demonstrate that our framework can effectively reduce the execution cost compared with other frameworks.

Keywords

Mobile cloud computing Application partition Task offloading Cloud selection Execution cost minimization 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61373178, 61373179, 61402381), Natural Science Key Foundation of Chongqing (cstc2015jcyjBX0094), the Fundamental Research Funds for the Central Universities (XDJK2013A018, XDJK2015C010, XDJK2015D023), and Natural Science Foundation of Chongqing (CSTC2016JCYJA0449), China Postdoctoral Science Foundation (2016M592619) and Chongqing Postdoctoral Science Foundation (XM2016002).

References

  1. 1.
  2. 2.
  3. 3.
    Chabrier, T., Tisserand, A.: On-the-fly multi-base recoding for ECC scalar multiplication without pre-computations. In: 2013 IEEE 21st Symposium on Computer Arithmetic, pp. 219–228 (2013)Google Scholar
  4. 4.
    Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26, 974–983 (2015)CrossRefGoogle Scholar
  5. 5.
    Chun, B.G., Ihm, S., Maniatis, P., Naik, M., Patti, A.: CloneCloud: elastic execution between mobile device and cloud. In: Conference on Computer Systems, pp. 301–314 (2011)Google Scholar
  6. 6.
    Cuervo, E., Balasubramanian, A., Cho, D.K., Wolman, A., Saroiu, S., Chandra, R., Bahl, P.: MAUI: making smartphones last longer with code offload. In: International Conference on Mobile Systems, Applications, and Services, pp. 49–62 (2010)Google Scholar
  7. 7.
    Guo, S., Xiao, B., Yang, Y., Yang, Y.: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9 (2016)Google Scholar
  8. 8.
    Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X.: Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: 2012 Proceedings IEEE INFOCOM, pp. 945–953 (2012)Google Scholar
  9. 9.
    Li, Y., Gao, W.: Code offload with least context migration in the mobile cloud. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1876–1884 (2015)Google Scholar
  10. 10.
    Liu, J., Ahmed, E., Shiraz, M., Gani, A., Buyya, R., Qureshi, A.: Application partitioning algorithms in mobile cloud computing: taxonomy, review and future directions. J. Netw. Comput. Appl. 48(C), 99–117 (2015)CrossRefGoogle Scholar
  11. 11.
    Khan, A.R., Othman, M., Madani, S.A., Khan, S.U.: A survey of mobile cloud computing application models. IEEE Commun. Surv. Tutor. 16(1), 393–413 (2014)CrossRefGoogle Scholar
  12. 12.
    Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)CrossRefGoogle Scholar
  13. 13.
    Terefe, M.B., Lee, H., Heo, N., Fox, G.C., Oh, S.: Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing. Pervasive Mob. Comput. 27(C), 75–89 (2016)CrossRefGoogle Scholar
  14. 14.
    Yang, L., Cao, J., Cheng, H., Ji, Y.: Multi-user computation partitioning for latency sensitive mobile cloud applications. IEEE Trans. Comput. 64(8), 2253–2266 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Yang, L., Cao, J., Tang, S., Li, T., Chan, A.T.S.: A framework for partitioning and execution of data stream applications in mobile cloud computing. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 794–802 (2012)Google Scholar
  16. 16.
    Yang, S., Kwon, D., Yi, H., Cho, Y., Kwon, Y., Paek, Y.: Techniques to minimize state transfer costs for dynamic execution offloading in mobile cloud computing. IEEE Trans. Mob. Comput. 13(11), 2648–2660 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.Department of Electrical and Computer EngineeringStony Brook UniversityStony BrookUSA

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