Skip to main content

Advertisement

Log in

An edge computational offloading architecture for ultra-low latency in smart mobile devices

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Computation offloading is a notable paradigm under Mobile Edge Computing (MEC) that significantly improves the performance of Smart Mobile Devices (SMDs) through the provision of computation, energy and storage services with the aid of edge servers. Recent studies under computation offloading pay less attention to SMDs and their fast-changing context conditions. Hence, this paper proposes, first, a novel Context-aware Computation Offloading (CaCO) architecture, particularly considering the execution time and battery consumption of SMDs when running resource-intensive tasks before proposing offloads. Secondly, an Efficient Genetic Algorithm (EGA) is presented to obtain the optimized solution for the formulated task allocation NP-hard problem in accessible time complexity. Extensive experiments conducted with real Android SMDs and simulation results compared with other baseline algorithms show that the proposed algorithm is superior in performance and could effectively reduce energy consumption and task completion latency.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Dolui, K., & Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In 2017 global internet of things summit (GIoTS), Geneva, Switzerland (pp. 1–6).

  2. Caprolu, M., Di Pietro, R., Lombardi, F., & Raponi, S. (2019). Edge computing perspectives: Architectures, technologies, and open security issues. In 2019 IEEE international conference on edge computing (EDGE), Milan, Italy (pp. 116–123).

  3. Alrowaily, M., & Lu, Z. (2018). Secure edge computing in IoT systems: Review and case studies. In 2018 IEEE/ACM symposium on edge computing (SEC), Seattle, USA (pp. 440–444).

  4. Ziegler, S. (2017). Considerations on IPv6 scalability for the Internet of Things—Towards an intergalactic Internet. In 2017 global internet of things summit (GIoTS), Geneva, Switzerland (pp. 1–4).

  5. Marah, B. D., Jin, Z., Ma, T., Alsabri, R., Anaadumba, R., Al-Dhelaan, A., & Al-Dhelaan, M. (2020). Smartphone architecture for edge-centric IoT analytics. Sensors, 20(3), 2020.

    Article  Google Scholar 

  6. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    Article  Google Scholar 

  7. Gill, Q. K., & Kaur, K. (2016). A computation offloading scheme for performance enhancement of smart mobile devices for mobile cloud computing. In 2016 international conference on next generation intelligent systems (ICNGIS), Kottayam, India (pp. 1–6).

  8. Mengistu, T., Alahmadi, A., Albuali, A., Alsenani, Y., & Che, D. (2017). A “no data center” solution to cloud computing. In 2017 IEEE 10th international conference on cloud computing (CLOUD), Honolulu, CA, USA (pp. 714–717).

  9. Shen, J., Zhou, T., He, D., Zhang, Y., Sun, X., & Xiang, Y. (2019). Block design-based key agreement for group data sharing in cloud computing. IEEE Transactions on Dependable and Secure Computing, 16(6), 996–1010.

    Article  Google Scholar 

  10. Jiang, C., Cheng, X., Gao, H., Zhou, X., & Wan, J. (2019). Toward computation offloading in edge computing: A survey. IEEE Access, 7, 131543–131558.

    Article  Google Scholar 

  11. Cuervo, E., Balasubramanian, A., Cho, D.-k., Wolman, A., Saroiu, S., Chandra, R., Bahl, P. (2010). MAUI: Making smartphones last longer with code offload. In Proceedings of the 8th international conference on mobile systems, applications, and services, San Francisco, California, USA.

  12. Chun, B.-G., Ihm, S., Petros, M., & Naik, M. (2010). CloneCloud: Boosting mobile device applications through cloud clone execution. arXiv:abs/1009.3088.

  13. Roelof, K., Nicholas, P., Thilo, K., & Henri, B. (2010). Cuckoo: A computation offloading framework for smartphones. In Mobile computing, applications, and services. MobiCASE 2010., Berlin, Heidelberg.

  14. Kosta, S., Aucinas, A., Hui, P., Mortier, R., & Zhang, X. (2012) ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In 2012 proceedings IEEE INFOCOM (pp. 945–953). https://doi.org/10.1109/INFCOM.2012.6195845.

  15. Wang, J., Wu, W., Liao, Z., Sangaiah, A. K., & Sherratt, R. S. (2019). An energy-efficient off-loading scheme for low latency in collaborative edge computing. IEEE Access, 7, 149182–149190.

    Article  Google Scholar 

  16. Akherfi, K., Gerndt, M., & Harroud, H. (2018). Mobile cloud computing for computation offloading: Issues and challenges. Applied Computing and Informatics, 14, 1–16.

    Article  Google Scholar 

  17. Zhao, B., Xu, Z., Chi, C., Zhu, S., & Cao G. (2010). Mirroring smartphones for good: A feasibility study. In International conference on mobile and ubiquitous systems: computing, networking, and services. MobiQuitous 2010, Sydney, Australia (vol. 73, pp. 26–38).

  18. Jia, M., Cao, J., & Liang, W. (2017). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725–737.

    Article  Google Scholar 

  19. Chouhan, S. (2019). Energy optimal partial computation offloading framework for mobile devices in multi-access edge computing. In 2019 international conference on software, telecommunications and computer networks (SoftCOM), Split, Croatia (pp. 1–6).

  20. Ning, Z., Dong, P., Kong, X., & Xia, F. (2019). A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things. IEEE Internet of Things Journal, 6(3), 4804–4814.

    Article  Google Scholar 

  21. Sheng, M., Wang, Y., Wang, X., & Li, J. (2020). Energy-efficient multiuser partial computation offloading with collaboration of terminals, radio access network, and edge server. IEEE Transactions on Communications, 68(3), 1524–1537.

    Article  Google Scholar 

  22. Wang, Y., Sheng, M., Wang, X., Wang, L., & Li, J. (2016). Mobile-edge computing: Partial computation offloading using dynamic voltage scaling. IEEE Transactions on Communications, 64(10), 4268–4282.

    Google Scholar 

  23. Wu, H., & Wolter, K. (2015). Software aging in mobile devices: Partial computation offloading as a solution. In 2015 IEEE international symposium on software reliability engineering workshops (ISSREW), Gaithersburg, MD, USA (pp. 125–131).

  24. Gu, B., Zhou, Z., Mumtaz, S., Frascolla, V., & Kashif Bashir, A. (2018). Context-aware task offloading for multi-access edge computing: matching with externalities. In 2018 IEEE global communications conference (GLOBECOM) (pp. 1–6). https://doi.org/10.1109/GLOCOM.2018.8647845.

  25. Breitbach, M., Schäfer, D., Edinger, J., & Becker, C. (2019). Context-aware data and task placement in edge computing environments. In 2019 IEEE international conference on pervasive computing and communications (PerCom) (pp. 1–10). https://doi.org/10.1109/PERCOM.2019.8767386

  26. Shahidinejad, A., Farahbakhsh, F., Ghobaei-Arani, M., et al. (2021). Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach. Journal of Grid Computing, 19, 18. https://doi.org/10.1007/s10723-021-09559-x

    Article  Google Scholar 

  27. Apolónia, N., Freitag, F., Navarro, L., Girdzijauskas, S., & Vlassov, V. (2017). Gossip-based service monitoring platform for wireless edge cloud computing. In 2017 IEEE 14th international conference on networking, sensing and control (ICNSC), Calabria (pp. 789–794).

  28. Nancy, J. J., Mani, T. S., Rohith, S., Saranraj, S., & Vigneswaran, T. (2020). Load balancing using load sharing technique in distribution system. In 2020 6th international conference on advanced computing and communication systems (ICACCS), Coimbatore, India (pp. 791–794).

  29. Kaur, S., & Sharma, T. (2018). Efficient load balancing using improved central load balancing technique. In 2018 2nd international conference on inventive systems and control (ICISC), Coimbatore (pp. 1–5).

  30. Chen, X., Chen, S., Zeng, X., Zheng, X., Zeng, Y., & Rong, C. (2017). Framework for context-aware computation offloading in mobile cloud computing. Journal of Cloud Computing, 6(1), 1–17.

    Article  Google Scholar 

  31. Abbas, I., Ahmad, M., Faizan, M., Arshed, W., & Khalid, J. (2020). Issues and challenges of cloud computing in performance augmentation for pervasive computing. In 2020 international conference on electrical, communication, and computer engineering (ICECCE), Istanbul, Turkey, (pp. 1–7).

  32. Yao, M., Chen, L., Liu, T., & Wu, J. (2019). Energy efficient cooperative edge computing with multi-source multi-relay devices. In 2019 IEEE 21st international conference on high performance computing and communications; IEEE 17th international conference on smart city; IEEE 5th international conference on data science and systems (HPCC/SmartCity/DSS) (pp. 865–870).

  33. Cao, X., Wang, F., Xu, J., Zhang, R., & Cui, S. (2019). Joint computation and communication cooperation for energy-efficient mobile edge computing. IEEE Internet of Things Journal, 6(3), 4188–4200.

    Article  Google Scholar 

  34. Deng, L., Yang, P. & Liu, W. (2019). An improved genetic algorithm. In 2019 IEEE 5th international conference on computer and communications (ICCC), Chengdu, China (pp. 47–51).

Download references

Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (No. 2021YFE014400), the National Natural Science Foundation of China (Grant Nos. 61602252, 11761074), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20160967), Project of Jilin Science and Technology Development for Leading Talent of Science and Technology Innovation in Middle and Young and Team Project (No. 20200301053RQ) and the Project through the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institution.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tinghuai Ma.

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

Osibo, B.K., Jin, Z., Ma, T. et al. An edge computational offloading architecture for ultra-low latency in smart mobile devices. Wireless Netw 28, 2061–2075 (2022). https://doi.org/10.1007/s11276-022-02956-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-02956-4

Keywords

Navigation