Advertisement

Energy-Efficient Computation Offloading for Multimedia Workflows in Mobile Cloud Computing

  • Tao Huang
  • Yi Chen
  • Shengjun Xue
  • Haojun Ji
  • Yuan Xue
  • Lianyong QiEmail author
  • Xiaolong Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 270)

Abstract

In recent years, mobile cloud computing (MCC) is utilized to process multimedia workflows due to the limitation of battery capacity of mobile devices, which influences the experience of multimedia applications on the mobile devices. Computation offloading based on cloudlet is introduced as a novel paradigm to relieve the high latency which offloading computation to remote cloud causes. However, it is still a challenge for mobile devices to offload computation of multimedia workflows in cloudlet-based cloud computation environment to reduce energy consumption, which meets time constraints at the same time. In view of the challenge, an energy-efficient computation offloading method of multimedia workflow with multi-objective optimization is proposed in this paper. Technically, an offloading method based on cloudlet using Differential Evolution (DE) algorithm is proposed to optimize the energy consumption of the mobile devices with time constraints. Finally, massive experimental evaluations and comparison analysis validate the efficiency of our proposed method.

Keywords

Energy-efficient Offloading Multimedia workflow Mobile Cloudlet DE 

Notes

Acknowledgement

This research is supported by the Research Project of Shanghai Meteorological Bureau Scientific under Grant No. TD201807 and the National Science Foundation of China under grant no. 61702277, no. 61672276, no. 61772283, no. 61402167 and no. 61672290, the Key Research and Development Project of Jiangsu Province under Grant No. BE2015154 and BE2016120, and Natural Science Foundation of Jiangsu Province (Grant No. BK20171458). Besides, this work is also supported by The Startup Foundation for Introducing Talent of NUIST, the open project from State Key Laboratory for Novel Software Technology.

References

  1. 1.
    Kaewmahingsa, K., Bhattarakosol, P.: Mobile cloud system: a solution for multimedia retrieval via mobile phones. In: International Conference on Computing & Convergence Technology, vol. 8652, no. 5, pp. 36–40 (2012)Google Scholar
  2. 2.
    Altamimi, M., Palit, R., Naik, K., Nayak, A.: Energy-as-a-Service (EaaS): on the efficacy of multimedia cloud computing to save smartphone energy. In: IEEE Fifth International Conference on Cloud Computing, pp. 764–771 (2012)Google Scholar
  3. 3.
    Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(4), 974–983 (2015)CrossRefGoogle Scholar
  4. 4.
    Kovachev, D., Yu, T., Klamma, R.: Adaptive computation offloading from mobile devices into the cloud. In: IEEE International Symposium on Parallel & Distributed Processing with Applications, pp. 784–791 (2012)Google Scholar
  5. 5.
    Liu, Y., Lee, M.: Adaptive multi-resource allocation for cloudlet-based mobile cloud computing system. IEEE Trans. Mob. Comput. 15(10), 2398–2410 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Xu, Z., Liang, W., Xu, W., et al.: Efficient algorithms for capacitated cloudlet placements. IEEE Trans. Parallel Distrib. Syst. 27(10), 2866–2880 (2016)CrossRefGoogle Scholar
  7. 7.
    Hazekamp, N., Kremer-Herman, N., Tovar, B., Meng, H., Choudhury, O.: Combining static and dynamic storage management for data intensive scientific workflows. IEEE Trans. Parallel Distrib. Syst. 29(2), 338–350 (2018)CrossRefGoogle Scholar
  8. 8.
    Liu, P., Wang, R., Ding, J., Yin, X.: Performance modeling and evaluating workflow of ITS: real-time positioning and route planning (1), 1–15 (2017)Google Scholar
  9. 9.
    Deng, S., Huang, L., Taheri, J., Zomaya, A.Y.: Computation offloading for service workflow in mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(12), 3317–3329 (2015)CrossRefGoogle Scholar
  10. 10.
    Zhang, J., et al.: Hybrid Computation offloading for smart home automation in mobile cloud computing. Pers. Ubiquit. Comput. 22(1), 121–134 (2018)CrossRefGoogle Scholar
  11. 11.
    He, D., Kumar, N., Khan, M.K., et al.: Efficient privacy-aware authentication scheme for mobile cloud computing services. IEEE Syst. J. 12(2), 1621–1631 (2018)CrossRefGoogle Scholar
  12. 12.
    Li, R., Shen, C., He, H., Xu, Z., Xu, C.Z.: A lightweight secure data sharing scheme for mobile cloud computing. IEEE Trans. Cloud Comput. 6(2), 344–357 (2018)CrossRefGoogle Scholar
  13. 13.
    Elgendy, I., Zhang, W., Liu, C., Hsu, C.: An efficient and secured framework for mobile cloud computing. IEEE Trans. Cloud Comput. (2018)Google Scholar
  14. 14.
    Xue, S., Peng, Y., Xu, X., Zhang, J., Shen, C., Ruan, F.: DSM: a dynamic scheduling method for concurrent workflows in cloud environment. Cluster Comput. 3, 1–14 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Tao Huang
    • 1
    • 2
  • Yi Chen
    • 3
  • Shengjun Xue
    • 1
    • 3
  • Haojun Ji
    • 2
  • Yuan Xue
    • 3
  • Lianyong Qi
    • 4
    Email author
  • Xiaolong Xu
    • 3
  1. 1.School of Computer Science and TechnologySilicon Lake CollegeSuzhouChina
  2. 2.Shanghai Jiading District Meteorological BureauJiadingChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  4. 4.School of Information Science and EngineeringQufu Normal UniversityQufuChina

Personalised recommendations