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Deep Learning with Enhanced Convergence and Its Application in MEC Task Offloading

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13156))

Abstract

As an emerging computing paradigm, the mobile edge computing (MEC) has become the top topic in various research fields. Nevertheless, task offloading, as a key issue in MEC environment, is still an immense challenge because it is often NP-hard. Currently, many researchers adopt deep learning frameworks to solve task offloading problem of MEC. Unfortunately, most of these works directly use various deep learning frameworks. It is insufficient consideration that how to improve the convergence performance of deep learning in solving MEC task offloading problem. To cope with this issue, we propose two methods to enhance the convergence of deep learning in this paper, which are named as uniform design method (UDM) and hadamard matrix method (HMM), respectively. UDM and HMM can enhance exploiting ability of the space near the specific offloading decision, benefiting to improve the convergence performance of deep learning algorithms. An improved deep learning algorithm is built by integrating UDM or HMM. The validity of our proposed algorithm is verified through extensive simulation experiments. The results show that our proposed algorithm can achieve better convergence performance than the benchmark algorithm under different learning rates and memory sizes.

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References

  1. Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)

    Article  Google Scholar 

  2. ETSI MEC: Mobile edge computing (MEC); framework and reference architecture. ETSI, DGS MEC 3 (2016)

    Google Scholar 

  3. Chen, Y., Lin, Y., Zheng, Z., Yu, P., Shen, J., Guo, M.: Preference-aware edge server placement in the Internet of Things. IEEE Internet Things J. 9, 1289–1299 (2021)

    Article  Google Scholar 

  4. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2016)

    Article  Google Scholar 

  5. Wang, X., Han, Y., Leung, V.C.M., Niyato, D., Yan, X., Chen, X.: Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(2), 869–904 (2020)

    Article  Google Scholar 

  6. Huang, L., Feng, X., Feng, A., Huang, Y., Qian, L.P.: Distributed deep learning-based offloading for mobile edge computing networks. Mob. Netw. Appl. 1–8 (2018)

    Google Scholar 

  7. Sharma, A.R., Kaushik, P.: Literature survey of statistical, deep and reinforcement learning in natural language processing. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 350–354. IEEE (2017)

    Google Scholar 

  8. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)

    Article  Google Scholar 

  9. Tassinari, P., et al.: A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comput. Electron. Agric. 182, 106030 (2021)

    Article  Google Scholar 

  10. He, Y., Yu, F.R., Zhao, N., Leung, V.C.M., Yin, H.: Software-defined networks with mobile edge computing and caching for smart cities: a big data deep reinforcement learning approach. IEEE Commun. Mag. 55(12), 31–37 (2017)

    Article  Google Scholar 

  11. Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., Zhuang, W.: Learning-based computation offloading for IoT devices with energy harvesting. IEEE Trans. Veh. Technol. 68(2), 1930–1941 (2019)

    Article  Google Scholar 

  12. Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., Bennis, M.: Performance optimization in mobile-edge computing via deep reinforcement learning. In: 88th IEEE Vehicular Technology Conference, VTC Fall 2018, Chicago, IL, USA, 27–30 August 2018, pp. 1–6. IEEE (2018)

    Google Scholar 

  13. Huang, L., Feng, X., Qian, L., Wu, Y.: Deep reinforcement learning-based task offloading and resource allocation for mobile edge computing. In: Meng, L., Zhang, Y. (eds.) MLICOM 2018. LNICSSITE, vol. 251, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00557-3_4

    Chapter  Google Scholar 

  14. Li, F., Yao, H., Du, J., Jiang, C., Yu, F.R.: Green communication and computation offloading in ultra-dense networks. In: 2019 IEEE Global Communications Conference, GLOBECOM 2019, Waikoloa, HI, USA, 9–13 December 2019, pp. 1–6. IEEE (2019)

    Google Scholar 

  15. Zhao, G., Xu, H., Zhao, Y., Qiao, C., Huang, L.: Offloading dependent tasks in mobile edge computing with service caching. In: 39th IEEE Conference on Computer Communications, INFOCOM 2020, Toronto, ON, Canada, 6–9 July 2020, pp. 1997–2006. IEEE (2020)

    Google Scholar 

  16. Ma, X., Zhou, A., Zhang, S., Wang, S.: Cooperative service caching and workload scheduling in mobile edge computing. In: 39th IEEE Conference on Computer Communications, INFOCOM 2020, Toronto, ON, Canada, 6–9 July 2020, pp. 2076–2085. IEEE (2020)

    Google Scholar 

  17. Mukherjee, M., Kumar, V., Lat, A., Guo, M., Matam, R., Lv, Y.: Distributed deep learning-based task offloading for uav-enabled mobile edge computing. In: 39th IEEE Conference on Computer Communications, INFOCOM Workshops 2020, Toronto, ON, Canada, July 6–9, 2020. pp. 1208–1212. IEEE (2020)

    Google Scholar 

  18. Ali, Z., Jiao, L., Baker, T., Abbas, G., Abbas, Z.H., Khaf, S.: A deep learning approach for energy efficient computational offloading in mobile edge computing. IEEE Access 7, 149623–149633 (2019)

    Article  Google Scholar 

  19. He, X., Lu, H., Huang, H., Mao, Y., Wang, K., Guo, S.: QoE-based cooperative task offloading with deep reinforcement learning in mobile edge networks. IEEE Wirel. Commun. 27(3), 111–117 (2020)

    Article  Google Scholar 

  20. Chen, J., Chen, S., Luo, S., Wang, Q., Cao, B., Li, X.: An intelligent task offloading algorithm (iTOA) for UAV edge computing network. Digital Commun. Netw. 6(4), 433–443 (2020)

    Article  Google Scholar 

  21. Yan, P., Choudhury, S.: Optimizing mobile edge computing multi-level task offloading via deep reinforcement learning. In: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7–11 June 2020, pp. 1–7. IEEE (2020)

    Google Scholar 

  22. Baek, J., Kaddoum, G.: Heterogeneous task offloading and resource allocations via deep recurrent reinforcement learning in partial observable multifog networks. IEEE Internet Things J. 8(2), 1041–1056 (2021)

    Article  Google Scholar 

  23. Li, Z., Hu, H., Hu, H., Huang, B., Ge, J., Chang, V.: Security and energy-aware collaborative task offloading in D2D communication. Future Gener. Comput. Syst. 118, 358–373 (2021)

    Article  Google Scholar 

  24. Yang, Q., Luo, X., Li, P., Miyazaki, T., Wang, X.: Computation offloading for fast CNN inference in edge computing. In: Proceedings of the Conference on Research in Adaptive and Convergent Systems, RACS 2019, Chongqing, China, 24–27 September 2019, pp. 101–106. ACM (2019)

    Google Scholar 

  25. Wang, Y., Kaitai, F.: A note on uniform distribution and experimental design. Kexue Tongbao (Chinese) 6, 485–489 (1981)

    MathSciNet  MATH  Google Scholar 

  26. Fang, K., Lin, D.K.J., Winker, P., Zhang, Y.: Uniform design: theory and application. Technometrics 42(3), 237–248 (2000)

    Article  MathSciNet  Google Scholar 

  27. Peng, H., Wu, Z., Deng, C.: Enhancing differential evolution with commensal learning and uniform local search. Chin. J. Electron. 26(4), 725–733 (2017)

    Article  Google Scholar 

  28. Sylvester, J.J.: Lx. Thoughts on inverse orthogonal matrices, simultaneous signsuccessions, and tessellated pavements in two or more colours, with applications to newton’s rule, ornamental tile-work, and the theory of numbers. Philos. Mag. 34, 461–475 (1867)

    Article  Google Scholar 

  29. Kimura, H.: New Hadamard matrix of order 24. Graphs Comb. 5(1), 235–242 (1989)

    Article  MathSciNet  Google Scholar 

  30. En.wikipedia.org: Hadamard matrix. https://en.wikipedia.org/wiki/Hadamard matrix

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61961021), the Science and Technology Project of Jiangxi Education Department (No. GJJ180251), and the Natural Science Foundation of Jiangxi Province (Nos. 20202BABL202036, 20202BABL202019).

Author information

Authors and Affiliations

  1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Zheng Wan & Xiaogang Dong

  2. School of Computer and Big Data Science, Jiujiang University, Jiujiang, 332005, China

    Xiaogang Dong & Changshou Deng

Authors
  1. Zheng Wan
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  2. Xiaogang Dong
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  3. Changshou Deng
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Corresponding author

Correspondence to Xiaogang Dong .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Wan, Z., Dong, X., Deng, C. (2022). Deep Learning with Enhanced Convergence and Its Application in MEC Task Offloading. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_24

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  • DOI: https://doi.org/10.1007/978-3-030-95388-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95387-4

  • Online ISBN: 978-3-030-95388-1

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