Skip to main content

Advertisement

Log in

Joint Downlink and Uplink Edge Computing Offloading in Ultra-Dense HetNets

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The deployment of ultra-dense heterogeneous networks (HetNets) are envisioned as the essentials to embrace of intelligent applications for the next-generation wireless networks. For ultra-dense HetNets, the small scale heterogeneous edge servers in macrocell and smallcell, decreasing downlink and uplink communication delay should be attracted more attention for latency-critical intelligent applications. Therefore, we aim at edge computation offloading of joint downlink (DL) and uplink (UL) together with communication and computing resource allocation. Then, we formulate the computation offloading optimization problem into minimizing the overall delay of task while saving energy consumption of user device. However,the joint downlink and uplink computing offloading problem with resource allocation is a mixed binary integer programming which is difficult to deal with. We convert the programming problem into resource allocation sub-problem and computing offloading sub-problem, and propose an efficient joint downlink and uplink offloading algorithm for ultra-dense HetNets. Numerical results validate the efficiency of the proposed algorithm in terms of the delay and energy saving of system.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Dong Z, Liu Y, Zhou H, et al (2017) An energy-efficient offloading framework with predictable temporal correctness. In: Proc ACM/IEEE symposium on edge computing (SEC), San Jose / Silicon Valley, CA, USA, October 12-14, pp 1–12

  2. Guenter Klas (2017) Edge computing and the role of cellular networks. Computer 50(10):40–49

    Article  Google Scholar 

  3. Shi W, Cao J, Zhang Q, et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  4. Mouradian C, Naboulsi D, Yangui S, et al (2018) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutorials 20(1):416–464

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Dinh HT, Lee C, Niyato D, et al (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611

    Article  Google Scholar 

  7. Hu YC, Patel M, Sabella D, et al (2015) Mobile edge computingA key technology towards 5G. ETSI White Paper :1–16

  8. Du J, Gelenbe E, Jiang C, et al (2017) Contract design for traffic offloading and resource allocation in heterogeneous Ultra-Dense networks. IEEE J Sel Areas Commun 35(11):2457–2467

    Article  Google Scholar 

  9. Zheng J, Li J, Wang N, et al (2017) Joint load balancing of downlink and uplink for eICIC in heterogeneous network. IEEE Trans Veh Technol 66(7):6388–6398

    Article  Google Scholar 

  10. Ksentini A, Taleb T, Chen M (2014) A Markov decision process-based service migration procedure for follow me cloud. In: Proc IEEE international conference on communications (ICC), pp 1350–1354

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

    Article  Google Scholar 

  12. Tong L, Li Y, Gao W (2016) A hierarchical edge cloud architecture for mobile computing. In: Proc international conference on computer communications (INFOCOM), pp 1–9

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

    Article  Google Scholar 

  14. Xiao Y, Krunz M (2017) Qoe and power efficiency tradeoff for fog computing networks with fog node cooperation. In: Proc international conference on computer communications (INFOCOM), pp 1–9

  15. Chen M, Dong M, Liang B, et al (2018) Resource sharing of a computing access point for multi-user mobile cloud offloading with delay constraints. IEEE Trans Mob Comput 17(12):2868–2881

    Article  Google Scholar 

  16. Chen M, Hao Y, Qiu M, et al (2016) Mobility aware caching and computation offloading in 5G ultra-dense cellular networks. Sensors 16(7):974

    Article  Google Scholar 

  17. 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– 6009

    Article  Google Scholar 

  18. Chen M, Dong M, Liang B, et al (2016) Joint offloading decision and resource allocation for mobile cloud with computing access point. International Conference on Acoustics, Speech, and Signal Processing :3516–3520

  19. Guo H, Liu J, Zhang J et al Mobile-edge computation offloading for ultra-dense IoT networks, IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2838584

    Article  Google Scholar 

  20. Chen M, Hao Y (2018) Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J Sel Areas Commun 36(3):1–11

    Article  Google Scholar 

  21. Boostanimehr H, Bhargava VK (2015) Joint Downlink and uplink aware cell association in HetNets with QoS provisioning[J]. IEEE Trans Wirel Commun 14(10):5388–5401

    Article  Google Scholar 

  22. Yao Q, Quek TQS, Huang A, et al (2017) Joint downlink and uplink energy minimization in wet-enabled networks. IEEE Trans Wirel Commun 16(10):6751–6765

    Article  Google Scholar 

  23. Wang C, Yu FR, Liang C, et al (2017) Joint computation offloading and interference management in wireless cellular networks with mobile edge computing. IEEE Trans Veh Technol 66(8):7432–7445

    Article  Google Scholar 

  24. Pochet Y, Wolsey LA (2006) Production planning by mixed integer programming. Springer, Berlin

    MATH  Google Scholar 

  25. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  26. Grant B, Boyd S (2011) CVX: matlab software for disciplined convex programming, http://cvxr.com/cvx/

  27. Liu Y, Niu D, Li B (2016) Delay-optimized video traffic routing in software-defined interdatacenter networks. IEEE Trans Multimedia 18(5):865–878

    Article  Google Scholar 

  28. Luenberger DG (1973) Introduction to linear and nonlinear programming. Addison-Wesley, Reading

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61701400, 61502387, 61503300, 41601353, 61572401 and 61672426), by Project Funded by China Postdoctoral Science Foundation (2017M613188), by Natural Science Basic Research Plan in Shaanxi Province of China (2017JQ6052, 2017JQ4003 and 17JK0783).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ling Gao or Lin Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, J., Gao, L., Wang, H. et al. Joint Downlink and Uplink Edge Computing Offloading in Ultra-Dense HetNets. Mobile Netw Appl 24, 1452–1460 (2019). https://doi.org/10.1007/s11036-019-01274-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01274-y

Keywords

Navigation