Advertisement

Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture

  • Zongkai Liu
  • Xiaoqiang YangEmail author
  • Jinxing Shen
Article
  • 22 Downloads

Abstract

As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5–4, while the time consumed by the traditional algorithm is 4.5–8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.

Keywords

Deep learning Multitask parallel processing architecture Mobile edge computing Network model Loading technology 

Notes

References

  1. 1.
    Tran TX, Hajisami A, Pandey P et al (2017) Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag 55(4):54–61CrossRefGoogle Scholar
  2. 2.
    Feng W, Jie X, Xin W et al (2017) Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans Wirel Commun 17(3):1784–1797Google Scholar
  3. 3.
    Wang C, Liang C, Yu FR et al (2017) Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans Wirel Commun 16(8):1CrossRefGoogle Scholar
  4. 4.
    Dinh TQ, Tang J, La QD et al (2017) Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Trans Commun 65(8):3571–3584Google Scholar
  5. 5.
    Zhou F, Wu Y, Hu RQ et al (2018) Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems. IEEE J Sel Areas Commun 36(9):1927–1941CrossRefGoogle Scholar
  6. 6.
    Mao Y, Zhang J, Song SH et al (2017) Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans Wirel Commun 16(9):5994–6009CrossRefGoogle Scholar
  7. 7.
    Sun Y, Sheng Z, Jie X (2017) EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35(11):2637–2646CrossRefGoogle Scholar
  8. 8.
    Ying H, Yu FR, Nan Z et al (2017) Software-defined networks with mobile edge computing and caching for smart cities: a big data deep reinforcement learning approach. IEEE Commun Mag 55(12):31–37CrossRefGoogle Scholar
  9. 9.
    Bi S, Zhang YJA (2017) Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans Wirel Commun 17(6):4177–4190CrossRefGoogle Scholar
  10. 10.
    Zhou Y, Yu FR, Jian C et al (2017) Resource allocation for information-centric virtualized heterogeneous networks with in-network caching and mobile edge computing. IEEE Trans Veh Technol 66(12):11339–11351CrossRefGoogle Scholar
  11. 11.
    Ke Z, Mao Y, Leng S et al (2017) Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Veh Technol Mag 12(2):36–44CrossRefGoogle Scholar
  12. 12.
    Liu J, Wan J, Bi Z et al (2017) A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun Mag 55(7):94–100CrossRefGoogle Scholar
  13. 13.
    Zhang G, Zhang W, Yu C et al (2018) Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Trans Ind Inform 14(10):4642–4655CrossRefGoogle Scholar
  14. 14.
    Kim S (2018) One-on-one contract game–based dynamic virtual machine migration scheme for mobile edge computing. Trans Emerg Telecommun Technol 29(1):e3204CrossRefGoogle Scholar
  15. 15.
    Ning Z, Wang X, Huang J (2018) Mobile edge computing-enabled 5G vehicular networks: toward the integration of communication and computing. IEEE Veh Technol Mag 14(1):54–61CrossRefGoogle Scholar
  16. 16.
    Ying H, Yu FR, Nan Z et al (2018) Secure social networks in 5G systems with mobile edge computing, caching, and device-to-device communications. IEEE Wirel Commun 25(3):103–109CrossRefGoogle Scholar
  17. 17.
    Zhang Z, Zhang W, Tseng FH (2019) Satellite mobile edge computing: improving QoS of high-speed satellite-terrestrial networks using edge computing techniques. IEEE Network 33(1):70–76CrossRefGoogle Scholar
  18. 18.
    Rodrigues TG, Suto K, Nishiyama H et al (2018) Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans Comput 67(9):1287–1300MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Carvalho GHS, Woungang I, Anpalagan A et al (2019) Analysis of joint parallelism in wireless and cloud domains on mobile edge computing over 5G systems. J Commun Netw 20(6):565–577CrossRefGoogle Scholar
  20. 20.
    Meng L, Yu R, Si P et al (2018) Energy-efficient machine-to-machine (M2M) communications in virtualized cellular networks with mobile edge computing (MEC). IEEE Trans Mob Comput 18(7):1541–1555Google Scholar
  21. 21.
    Zhang H, Chen Z, Wu J et al (2019) FRRF: a fuzzy reasoning routing-forwarding algorithm using mobile device similarity in mobile edge computing-based opportunistic mobile social networks. IEEE Access 7:35874–35889CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Army Engineering University of PLANanjingChina

Personalised recommendations