Skip to main content

Advertisement

SpringerLink
Log in

Price elasticity log-log model for cost optimization in D2D underlay mobile edge computing system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The development of 5G/6G aims to provide high mobile computing with ultra-reliable low latency. The implementation of latency-sensitive computing applications is causing the overall cost management of the network to be a challenging issue. A Device-to-Device (D2D) underlay mobile edge computation offloading system has been developed to achieve these goals. This work intends to create a mobile edge computing (MEC)-D2D underlay system with cost optimization using the price elasticity log-log model (PEL2M). In this system, the task computation of each device can be partially offloaded to the edge server and nearby mobile helper. This work focuses on minimizing the computation cost of the network with computation and communication constraints. The optimization problem P1 is formulated as a mixed-integer nonlinear programming problem (MINLP). The problem P1 is split up into two sub-problems denoted by P2 and P3, respectively. The solution to P2 optimizes the task division ratio to meet delay tolerance. The solution to P3 minimizes energy consumption by using the price elasticity log-log model (PEL2M). Numerical results show that the proposed PEL2M better than MEC system with binary offloading (MECBO), MEC system with partial offloading (MECPO), Mobile edge computing and device-to-device underlay system with partial offloading (MECD2DUPO), Brute-force algorithm and Hungarian algorithm in terms of reduction in energy consumption by 72.71, 73.24, 66, 70.15, and 66.30%, and the latency is reduced by 88.4, 16.89, 9.5, 11.02, and 9%, respectively. The proposed algorithm may be applied to solve various latency-constrained applications in medical fields, vehicular communication, etc.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Zhang T, Chen W (2021) Computation offloading in heterogeneous mobile edge computing with energy harvesting. IEEE Trans Green Commun Netw 5(1):552–565. https://doi.org/10.1109/TGCN.2021.3050414

    Article  Google Scholar 

  2. Peng J, Qiu H, Cai J, Xu W, Wang J (2021) D2D-assisted multi-user cooperative partial offloading, transmission scheduling and computation allocating for MEC. IEEE Trans Wirel Commun 20(8):4858–4873. https://doi.org/10.1109/TWC.2021.3062616

    Article  Google Scholar 

  3. Fang T, Yuan F, Ao L, Chen J (2021) Joint task offloading, D2D pairing and resource allocation in device-enhanced MEC: a potential game approach. IEEE Internet Things J 9:3226–3237. https://doi.org/10.1109/JIOT.2021.3097754

    Article  Google Scholar 

  4. Hu S, Li G (2020) Dynamic request scheduling optimization in mobile edge computing for IoT applications. IEEE Internet Things J 7(2):1426–1437. https://doi.org/10.1109/JIOT.2019.2955311

    Article  Google Scholar 

  5. Chen CL, Brinton CG, Aggarwal V (2021) Latency minimization for mobile edge computing networks. IEEE Trans Mob Comput 1233:1–15. https://doi.org/10.1109/TMC.2021.3117511

    Article  Google Scholar 

  6. Liu J, Luo K, Zhou Z, Chen X (2019) ERP: edge resource pooling for data stream mobile computing. IEEE Internet Things J 6(3):4355–4368. https://doi.org/10.1109/JIOT.2018.2882588

    Article  Google Scholar 

  7. Li Y, Xu G, Yang K, Ge J, Liu P, Jin Z (2020) Energy efficient relay selection and resource allocation in D2D-enabled mobile edge computing. IEEE Trans Veh Technol 69(12):15800–15814. https://doi.org/10.1109/TVT.2020.3036489

    Article  Google Scholar 

  8. Yang Y, Long C, Wu J, Peng S, Li B (2021) D2D-enabled mobile-edge computation offloading for multiuser IoT network. IEEE Internet Things J 8(16):12490–12504. https://doi.org/10.1109/JIOT.2021.3068722

    Article  Google Scholar 

  9. He Y, Ren J, Yu G, Cai Y (2019) D2D communications meet mobile edge computing for enhanced computation capacity in cellular networks. IEEE Trans Wirel Commun 18(3):1750–1763. https://doi.org/10.1109/TWC.2019.2896999

    Article  Google Scholar 

  10. Li B, Fei Z, Shen J, Jiang X, Zhong X (2019) Dynamic offloading for energy harvesting mobile edge computing: architecture, case studies, and future directions. IEEE Access 7:79877–79886. https://doi.org/10.1109/ACCESS.2019.2922362

    Article  Google Scholar 

  11. Yu S, Dab B, Movahedi Z, Langar R, Wang L (2020) A socially-aware hybrid computation offloading framework for multi-access edge computing. IEEE Trans Mob Comput 19(6):1247–1259. https://doi.org/10.1109/TMC.2019.2908154

    Article  Google Scholar 

  12. Chen L, Wu J, Zhang XX, Zhou G (2021) TARCO: two-stage auction for D2D Relay aided computation resource allocation in HetNet. IEEE Trans Serv Comput 14(1):286–299. https://doi.org/10.1109/TSC.2018.2792024

    Article  Google Scholar 

  13. Xing H, Liu L, Xu J, Nallanathan A (2019) Joint task assignment and resource allocation for D2D-enabled mobile-edge computing. IEEE Trans Commun 67(6):4193–4207. https://doi.org/10.1109/TCOMM.2019.2903088

    Article  Google Scholar 

  14. Saleem U, Liu Y, Jangsher S, Tao X, Li Y (2020) Latency minimization for D2D-enabled partial computation offloading in mobile edge computing. IEEE Trans Veh Technol 69(4):4472–4486. https://doi.org/10.1109/TVT.2020.2978027

    Article  Google Scholar 

  15. Lai W, Member S, Wang Y, Member S, Lin H, Li J (2020) Efficient resource allocation and power control for LTE-A D2D communication with pure D2D model. IEEE Trans Veh Technol 69(3):3202–3216. https://doi.org/10.1109/TVT.2020.2964286

    Article  Google Scholar 

  16. Tran Q-N, Vo N-S, Bui M-P, Phan T-M, Nguyen Q-A, Duong TQ (2021) Spectrum sharing and power allocation optimised multi-hop multi-path D2D video delivery in beyond 5G networks. IEEE Trans Cogn Commun Netw 8:919–930. https://doi.org/10.1109/tccn.2021.3133838

    Article  Google Scholar 

  17. Wang Z, Sun L, Zhang M, Pang H, Tian E, Zhu W (2017) Propagation- and mobility-aware D2D social content replication. IEEE Trans Mob Comput 16(4):1107–1120. https://doi.org/10.1109/TMC.2016.2582159

    Article  Google Scholar 

  18. Fan W, Teng D, Wu F, Liu Y (2020) PageRank-based multi-hop computation offloading in D2D networks. IEEE Netw Lett 2(4):195–198. https://doi.org/10.1109/lnet.2020.3037822

    Article  Google Scholar 

  19. Zhang X et al (2017) Information caching strategy for cyber social computing based wireless networks. IEEE Trans Emerg Top Comput 5(3):391–402. https://doi.org/10.1109/TETC.2017.2699695

    Article  MathSciNet  Google Scholar 

  20. Sun M, Xu X, Tao X, Zhang P (2020) Large-scale user-assisted multi-task online offloading for latency reduction in D2D-enabled heterogeneous Networks. IEEE Trans Netw Sci Eng 7(4):2456–2467. https://doi.org/10.1109/TNSE.2020.2979511

    Article  MathSciNet  Google Scholar 

  21. Thimmapuram PR, Kim J, Botterud A, Nam Y (2010) Modeling and simulation of price elasticity of demand using an agent-based model. Innov Smart Grid Technol Conf ISGT 2010:1–8. https://doi.org/10.1109/ISGT.2010.5434739

    Article  Google Scholar 

  22. Petricek M, Chalupa S, Melas D (2021) Model of price optimization as a part of hotel revenue management—stochastic approach. Mathematics 9:1552

    Article  Google Scholar 

  23. Güneri Öİ, Durmus B, Aydın D (2019) Different approaches to solution of the assignment problem using r program. J Math Stat Sci 5:129–145

    Google Scholar 

Download references

Acknowledgements

There is no acknowledgement.

Author information

Authors and Affiliations

  1. Wireless Sensor Network Lab, Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, India

    Aakansha Garg & Rajeev Arya

  2. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India

    Maheshwari Prasad Singh

Authors
  1. Aakansha Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maheshwari Prasad Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aakansha Garg.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, A., Arya, R. & Singh, M.P. Price elasticity log-log model for cost optimization in D2D underlay mobile edge computing system. J Supercomput 79, 7094–7131 (2023). https://doi.org/10.1007/s11227-022-04928-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04928-z

Keywords

Search

Navigation