Skip to main content
SpringerLink
Log in

A task offloading algorithm for cloud-edge collaborative system based on Lyapunov optimization

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Due to the limitation of computing resources and storage resources, mobile edge computing cannot cope with the massive data generated by the Industrial Internet of Things (IIoT). However, traditional mobile cloud computing has rich computing resources. Therefore, through the construction of cloud computing and edge computing collaborative system, high bandwidth and low latency network services for the Internet of things can be provided. Based on Lyapunov optimization theory, the resource allocation and power consumption in cloud-edge collaborative system are investigated in this paper. Firstly, a cloud-edge collaboration architecture is proposed, then by establishing the dynamic queue model of cloud computing server and edge computing server, and combining with the system power function to form a drift plus penalty function framework, the problem is reduced to a constrained optimization problem. Finally, the offloading algorithm based on congestion is given. The simulation results show that the proposed optimization scheme can effectively reduce the overall power consumption and congestion of cloud-edge collaborative system.

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

Similar content being viewed by others

Data availability

The authors declare that all data supporting the findings of this study are available within the article.

References

  1. Xu, L., He, W., Li, S.: Internet of things in industries. IEEE Trans. Ind. Inf. 10(4), 2233–2243 (2014)

    Article  Google Scholar 

  2. Lv, L., Yang, Z., Zhang, L.: Research on offloading strategy of mobile computing tasks based on contract design. Control Decision 34(11), 97–105 (2019)

    Google Scholar 

  3. Tian, Z., Gao, X., Su, S., et al.: Evaluating reputation management schemes of internet of vehicles based on evolutionary game theory. IEEE Transactions on Vehicular Technology 68(6), 5971–5980 (2019)

    Article  Google Scholar 

  4. Tian, Z., Su, S., Shi, W., et al.: A data-driven method for future internet route decision modeling. Future Gener. Comput. Syst. 95, 212–220 (2019)

    Article  Google Scholar 

  5. Gu, Z., Wang, L., Chen, X., et al.: Epidemic risk assessment by a novel communication station based method. IEEE Trans. Netw. Sci. Eng. https://doi.org/10.1109/TNSE.2021.3058762 (2021)

    Article  Google Scholar 

  6. Gu, Z., Li, H., Deng, L., et al.: Iepsbp: A cost-efficient image encryption algorithm based on parallel chaotic system for green IoT. IEEE Trans. Green Commun. Netw. https://doi.org/10.1109/TGCN.2021.3095707 (2021)

  7. Lv, L., Yang, Z., Zhang, L., et al.: Multi-party transaction framework for drone services based on alliance blockchain in smart cities. J. Inf. Secur. Appl. 58(4), 102792 (2021)

    Google Scholar 

  8. Xia, W., Zhang, J., Quek, T., et al.: Mobile edge cloud-based industrial internet of things: Improving edge intelligence with hierarchical sdn controllers. IEEE Vehic. Technol. Mag. 15(1), 36–45 (2020)

    Article  Google Scholar 

  9. Jing, M., Hua, Z., Liu, C., et al.: Study on edge-cloud collaborative production scheduling based on enterprises with multi-factory. IEEE Access 8(1), 30069–30080 (2020)

    Google Scholar 

  10. Bai, Y., Huang, Y., Chen, S., et al.: Cloud edge intelligence: Edge computing method of power system operation control and its application status and prospect. Acta Automatica Sinica 46(3), 397–410 (2020)

    Google Scholar 

  11. Lou, P., Liu, S., Hu, J., et al.: Intelligent machine tool based on edge-cloud collaboration. IEEE Access 8, 139953–139965 (2020)

    Article  Google Scholar 

  12. Zhang, Y., Wang, X., He, H., et al.: A transfer learning-based high impedance fault detection method under a cloud-edge collaboration framework. IEEE Access 8, 165099–165110 (2020)

    Article  Google Scholar 

  13. Zhang, K., Huang, W., Hou, X., et al.: A fault diagnosis and visualization method for high-speed train based on edge and cloud collaboration. Appl. Sci. 11(3), 1–16 (2021)

    Article  Google Scholar 

  14. Yang, C., Lan, S., Wang, L., et al.: Big data driven edge-cloud collaboration architecture for cloud manufacturing: A software defined perspective. IEEE Access 8(1), 45938–45950 (2020)

    Article  Google Scholar 

  15. Sun, L., Wang, J., Lin, B.: Task allocation strategy for mec-enabled iiots via bayesian network based evolutionary computation. IEEE Trans. Ind. Inf. 17(5), 3441–3449 (2021)

    Article  Google Scholar 

  16. Wu, H., Lyu, X., Tian, H.: Online optimization of wireless powered mobile-edge computing for heterogeneous industrial internet of things. IEEE Internet Things J. 6(6), 9880–9892 (2019)

    Article  Google Scholar 

  17. Wu, H., Lyu, X., Tian, H.: Energy-efficient resource allocation for blockchain-enabled industrial internet of things with deep reinforcement learning. IEEE Internet of Things J. 8(4), 2318–2329 (2021)

    Article  Google Scholar 

  18. Li, C., Sun, H., Tang, H., Luo, Y.: Adaptive resource allocation based on the billing granularity in edge-cloud architecture. Comput. Commun. 145(SEP), 29–42 (2019)

  19. Gu, X., Zhang, G., Cao, Y.: Cooperative mobile edge computingloud computing in internet of vehicle: Architecture and energy efficient workload allocation. Trans. Emerg. Telecommun. Technol. 22(4), 1–21 (2020)

    Google Scholar 

  20. Wu, H., Zhang, Z., Guan, C., Wolter, K., Xu, M.: Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet Things J. 5(3), 1381–1394 (2020)

    Google Scholar 

  21. Lv, L., Chen, J., Zhang, Z., Wang, B., Zhang, L.: A numerical solution of a class of periodic coupled matrix equations. J. Franklin Inst. 358(3), 2039–2059 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang, L., Tang, S., Lv, L.: An finite iterative algorithm for sloving periodic sylvester bimatrix equations. J. Franklin Inst. 357(15), 10757–10772 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  23. Lv, L., Tang, S., Zhang, L.: Parametric solutions to generalized periodic sylvester bimatrix equations. J. Franklin Inst. 357(6), 3601–3621 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  24. Lv, L., Zhang, Z., Zhang, L.: A parametric poles assignment algorithm for second-order linear periodic systems. J. Franklin Inst. 354(18), 8057–8071 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  25. Lv, L., Zheng, C., Zhang, L., Du, X., Guizani, M., Tian, Z.: Contract and lyapunov optimization based load scheduling and energy management for uav charging stations. IEEE Trans. Green Commun. Netw. 7(9), 8099–8110 (2021)

    Google Scholar 

  26. Zhang, L., Huang, Z., Liu, W., Guo, Z., Zhang, Z.: Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. J. Clean. Prod. 298, 126776 (2021)

    Article  Google Scholar 

  27. Zhang, L., Xu, C., Gao, Y., Han, Y., Tian, Z.: Improved dota2 lineup recommendation model based on a bidirectional lstm. Tsinghua Sci. Technol. 25(6), 712–720 (2020)

    Article  Google Scholar 

  28. Gu, Z., Hu, W., Zhang, C., Lu, H., Yin, L., Wang, L.: Towards understanding vulnerability of deep neural networks. IEEE Trans. Netw. Sci. Eng. 8(2), 921–932 (2021)

    Article  Google Scholar 

  29. Lv, L., Wu, Z., Zhang, J., Tan, Z., Zhang, L., Tian, Z.: A vmd and lstm based hybrid model of load forecasting for power grid security. IEEE Trans. Ind. Inf. https://doi.org/10.1109/TII.2021.3130237 (2022)

  30. Zhang, L., Huo, Y., Ge, Q., Ma, Y., Ouyang, W.: A privacy protection scheme for iot big data based on time and frequency limitation. Wirel. Commun. Mobile Comput. 2021(3), 5545648 (2021)

    Google Scholar 

  31. Han, D., Chen, J., Zhang, L., et al.: A deletable and modifiable blockchain scheme based on record verification trees and the multisignature mechanism. CMES-Comput. Model. Eng. Sci. 128(1), 223–245 (2021)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Programs of National Natural Science Foundation of China (Grant No. 41401386), and Team Project Funding of Scientific Research Innovation for Colleges and Universities in Henan Province (Grant No. 21IRTSTHN017).

Author information

Authors and Affiliations

  1. School of Computer, Henan University of Engineering, Zhengzhou, 450011, China

    Jixun Gao

  2. Institute of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

    Rui Chang

  3. SAIC Motor Passenger Vehicle Company Zhengzhou Powertrain Branch, Zhengzhou, 450000, China

    Zhipeng Yang

  4. School of Electrical Information Engineering, Henan Institute of Engineering, Zhengzhou, 451191, China

    Quanzheng Huang

  5. Zhengzhou University of Technology, Zhengzhou, 451191, China

    Yuanyuan Zhao

  6. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072, China

    Yu Wu

  7. Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China

    Yu Wu

Authors
  1. Jixun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhipeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quanzheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuanyuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yu Wu.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Ethical approval

The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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

Gao, J., Chang, R., Yang, Z. et al. A task offloading algorithm for cloud-edge collaborative system based on Lyapunov optimization. Cluster Comput 26, 337–348 (2023). https://doi.org/10.1007/s10586-022-03563-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-022-03563-w

Keywords

Search

Navigation