Learning for Smart Edge: Cognitive Learning-Based Computation Offloading

Abstract

With the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by offloading computing tasks to edge cloud. Furthermore, with the complexity of computing scenarios in edge cloud, deep learning based on computation offloading scheme has attracted wide attention. However, all the learning-based offloading scheme does not consider the where and how to run the offloading scheme itself. Thus, in this paper, we consider the problem of running the learning-based computation offloading scheme for the first time and propose the learning for smart edge architecture. Then, we give the computation offloading optimization problem of mobile devices under multi-user and multi edge cloud scenarios. Furthermore, we propose cognitive learning-based computation offloading (CLCO) scheme for this problem. Finally, experimental results show that compared with other offloading schemes, the CLCO scheme has lower task duration and energy consumption.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. 1.

    Liu H, Zhang B, Su X, Ma J, Wang W, Leung K (2017) Energy-aware Participant Selection for Smartphone-enabled Mobile Crowd Sensing. IEEE Syst J 11(3):1435–1446

    Article  Google Scholar 

  2. 2.

    Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375

    Article  Google Scholar 

  3. 3.

    Hao Y, Peng L, Hu H, Hassan M, Atif A (2017) Energy harvesting based body area networks for smart health. Sensors 17(7):1602

    Article  Google Scholar 

  4. 4.

    Zhang Y, Gravina R, Lu H, Massimo V, Giancarlo F (2018) PEA: Parallel Electrocardiogram-based authentication for smart healthcare systems. J Netw Comput Appl 117:10–16

    Article  Google Scholar 

  5. 5.

    Liu H, Zhao J, Zhang H, Guo S, Leung K, Crowcroft J (2017) Energy-Efficient Event detection by participatory sensing under budget constraints. IEEE Syst J 11(4):2490–2501

    Article  Google Scholar 

  6. 6.

    Liu H, Fan J, Hui P, Wu J, Leung K (2015) Towards QoI and Energy-Efficiency in Participatory Crowdsourcing. IEEE Trans Veh Technol 64(10):4684–4700

    Article  Google Scholar 

  7. 7.

    Ge X, Yang B, Ye J, Mao G, Wang C, Han T (2015) Spatial spectrum and energy efficiency of random cellular networks. IEEE Trans Commun 63(3):1019–1030

    Article  Google Scholar 

  8. 8.

    Jiang H, Cai C, Ma X, Yang Y, Liu Q (2018) Smart home based on WiFi sensing: a survey. IEEE Access 6(6):13317– 13325

    Article  Google Scholar 

  9. 9.

    Ge X, Pan L, Li Q, Mao G, Tu S (2017) Multi-Path Cooperative communications networks for augmented and virtual reality transmission. IEEE Trans Multimed 19(10):2345–2358

    Article  Google Scholar 

  10. 10.

    Yu N, Kaoru S (2018) Per-Flow Throughput fairness in ring aggregation network with multiple edge routers. Big Data Cogn Comput 2(3):17

    Article  Google Scholar 

  11. 11.

    Chen M, Hao Y, Hu L, Hossain M, Ghoneim A (2018) An adaptive fusion strategy for distributed information estimation over cooperative Multi-Agent networks. IEEE Trans Inf Theory 63(5):3076–3091

    MathSciNet  Google Scholar 

  12. 12.

    Tian D, Zhou J, Sheng Z (2017) Edge-cocaco: Towards Joint Optimization of Computation, Caching and Communication on Edge Cloud. IEEE Wirel Commun 25(3):21–27

    Google Scholar 

  13. 13.

    Tong L, Li Y, Gao W (2016) A hierarchical edge cloud architecture for mobile computing. Proceedings of the 35th IEEE Conference on Computer Communications, pp 399–400

  14. 14.

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

    Article  Google Scholar 

  15. 15.

    Hany F, Robert J, Gary B (2018) Fog Computing and the Internet of Things: A Review. Big Data Cogn Comput 2(2):10

    Article  Google Scholar 

  16. 16.

    Chen M, Hao Y, Hu L, Huang K, Lau NK (2017) Green andMobilityaware Caching in 5G Networks. IEEE Trans Wirel Commun 16(12):8347–8361

    Article  Google Scholar 

  17. 17.

    Wang C, Li Y, Jin D (2014) Mobility-Assisted Opportunistic computation offloading. IEEE Commun Lett 18(10):1779–1782

    Article  Google Scholar 

  18. 18.

    Chen M, Hao Y, Li Y, Lai C, Wu D (2015) On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Commun Mag 53(6):18–24

    Article  Google Scholar 

  19. 19.

    Chun B, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. Proceedings of the sixth conference on Computer systems, pp 301–314

  20. 20.

    Barbera M, Kosta S, Mei A, Stefa J (2013) To offload or not to offload the bandwidth and energy costs of mobile cloud computing. INFOCOM, 2013 Proceedings IEEE, pp 1285–1293

  21. 21.

    Kollenstart M, Harmsma E, Langius E, Andrikopo V, Lazovik A (2018) Adaptive provisioning of heterogeneous cloud resources for big data processing. Big Data Cogn Comput 2(3):15

    Article  Google Scholar 

  22. 22.

    Chen M, Hao Y, Qiu M, Song J, Wu D, Humar I (2016) Mobility-aware Caching and Computation Offloading in 5G Ultradense Cellular Networks. Sensors 16(7):974–987

    Article  Google Scholar 

  23. 23.

    Li Y, Wang W (2014) Can mobile cloudlets support mobile applications?. In: 2014 Proceedings IEEE INFOCOM, pp 1060–1068

  24. 24.

    Chen M, Qian Y, Hao Y, Song L (2018) Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis. IEEE Wirel Commun 25(1):70–75

    Article  Google Scholar 

  25. 25.

    Joel RB (2018) A deep learning model of perception in Color-Letter synesthesia. Big Data Cogn Comput 2 (1):8

    MathSciNet  Article  Google Scholar 

  26. 26.

    Chen M, Hao Y (2018) Task Offloading for Mobile Edge Computing in Software Defined Ultra-dense Network. IEEE J Sel Areas Commun 36(3):587–597

    Article  Google Scholar 

  27. 27.

    Sun Y, Zhou S, Xu J (2017) EMM: Energy-Aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35(11):2637–2646

    Article  Google Scholar 

  28. 28.

    Daniel B (2018) Reimaging research methodology as data science. Big Data Cogn Comput 2(1):4

    Article  Google Scholar 

  29. 29.

    Zheng Y, Wu D, Ke Y, Yang C, Chen M, Zhang G (2017) EMM: Energy-Aware mobility management for mobile edge computing in ultra dense networks. ZhangYuanhuan2017 27(8):1777–1789

    Google Scholar 

  30. 30.

    Wang D, Peng Y, Ma X, Ding W, Jiang H, Liu J (2018) Adaptive Wireless Video Streaming based on Edge Computing: Opportunities and Approaches. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2018.2828426

  31. 31.

    Chen X, Pu L, Gao L, Wu W, Wu D (2017) Exploiting massive D2D collaboration for Energy-Efficient mobile edge computing. IEEE Wirel Commun 24(4):64–71

    Article  Google Scholar 

  32. 32.

    Kang K, Chen L, Yi H, Wang B, Sha M (2017) Real-Time Information derivation from big sensor data via edge computing. Big Data Cogn Comput 1(1):5

    Article  Google Scholar 

  33. 33.

    Chen M, Miao Y, Hao Y, Hwang K (2017) Narrow band internet of things. IEEE Access 5:20557–20577

    Article  Google Scholar 

  34. 34.

    Ge X, Sun Y, Gharavi H, John T (2018) Joint Optimization of Computation and Communication Power in Multi-user Massive MIMO Systems. IEEE Trans Wirel Commun 17(6):4051–4063

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to M. Shamim Hossain.

Additional information

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, for funding this research group No. (RG-1437-042).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hao, Y., Jiang, Y., Hossain, M.S. et al. Learning for Smart Edge: Cognitive Learning-Based Computation Offloading. Mobile Netw Appl 25, 1016–1022 (2020). https://doi.org/10.1007/s11036-018-1119-7

Download citation

Keywords

  • Computation offloading
  • Cognitive learning
  • Edge computing
  • Communication