Skip to main content

An Evolutionary Game Theoretic-Based Approach to Task Offloading in Hybrid Vehicular Cloud-Edge Environment

  • Conference paper
  • First Online:
Web Services – ICWS 2024 (ICWS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15428))

Included in the following conference series:

  • 74 Accesses

Abstract

Vehicle Edge Computing (VEC) is a novel computing paradigm that addresses the computational demands of intelligent vehicles by offloading tasks to edge servers. In a VEC environment, edge servers’ limited storage and processing capacity require a sensible task offloading strategy, where only a part of computing requirement can be offloaded directly to the VEC server and the remaining to the remote cloud. A primary challenge in this context is the creation of an effective and responsive task offloading algorithm that improves the utility. This study proposes an evolutionary game theoretic-based approach, utilizing a Dynamical-Resource Evolutionary Game (DREG) algorithm for decentralized task offloading. DREG leverages the Evolutionary Stable Strategy(ESS) and Adaptive Resource Allocation(ARA) method to optimize response delay and energy cost while increasing success rate. Experimental results indicate that DREG outperforms traditional methods across various performance metrics.

This work was supported in part by the grants from Science and Technology Program of Sichuan Province under Grant No.2024NSFTD0008, and in part by the Young Scientists Fund of the Natural Science Foundation of Henan Province under Grant No.242300421700.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang, D., Wang, W., Zhang, J., Zhang, T., Du, J., Yang, C.: Novel edge caching approach based on multi-agent deep reinforcement learning for internet of vehicles. IEEE Trans. Intell. Transp. Syst. (2023)

    Google Scholar 

  2. Truong, T.P., et al.: Partial computation offloading in Noma-assisted mobile-edge computing systems using deep reinforcement learning. IEEE Internet Things J. 8(17), pp. 13196–13208 (2021)

    Google Scholar 

  3. Xu, X., Chen, P., Xia, Y., Long, M., Peng, Q., Long, T.: MRoCO: a novel approach to structured application scheduling with a hybrid vehicular cloud-edge environment. In: 2022 IEEE International Conference on Services Computing (SCC), pp. 84–92. IEEE (2022)

    Google Scholar 

  4. Smith, J.M., Price, G.R.: The logic of animal conflict. Nature 246(5427), 15–18 (1973)

    Article  Google Scholar 

  5. Karimi, E., Chen, Y., Akbari, B.: Task offloading in vehicular edge computing networks via deep reinforcement learning. Comput. Commun. 189, 193–204 (2022)

    Article  Google Scholar 

  6. Gilly, K., Mishev, A., Filiposka, S., Alcaraz, S.: Offloading edge vehicular services in realistic urban environments. IEEE Access 8, 11491–11502 (2020)

    Google Scholar 

  7. Xue, Z., Liu, C., Liao, C., Han, G., Sheng, Z.: Joint service caching and computation offloading scheme based on deep reinforcement learning in vehicular edge computing systems. IEEE Trans. Vehic. Technol. (2023)

    Google Scholar 

  8. Zhou, Z., Feng, J., Chang, Z., Shen, X.: Energy-efficient edge computing service provisioning for vehicular networks: a consensus ADMM approach. IEEE Trans. Veh. Technol. 68(5), 5087–5099 (2019)

    Article  Google Scholar 

  9. Zhu, X., Luo, Y., Liu, A., Bhuiyan, M.Z.A., Zhang, S.: Multiagent deep reinforcement learning for vehicular computation offloading in IoT. IEEE Internet Things J. 8(12), 9763–9773 (2020)

    Article  Google Scholar 

  10. Du, J., Yu, F.R., Chu, X., Feng, J., Lu, G.: Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Trans. Veh. Technol. 68(2), 1079–1092 (2018)

    Article  Google Scholar 

  11. Liu, Y., Wang, S., Huang, J., Yang, F.: A computation offloading algorithm based on game theory for vehicular edge networks. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018)

    Google Scholar 

  12. Tang, C., Wu, H.: Joint optimization of task caching and computation offloading in vehicular edge computing. Peer-to-Peer Netw. Appl. 15(2), 854–869 (2021). https://doi.org/10.1007/s12083-021-01252-w

    Article  Google Scholar 

  13. Dai, P., Hu, K., Wu, X., Xing, H., Yu, Z.: Asynchronous deep reinforcement learning for data-driven task offloading in MEC-empowered vehicular networks. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1–10. IEEE (2021)

    Google Scholar 

  14. Huang, X., He, L., Chen, X., Wang, L., Li, F.: Revenue and energy efficiency-driven delay-constrained computing task offloading and resource allocation in a vehicular edge computing network: a deep reinforcement learning approach. IEEE Internet Things J. 9(11), 8852–8868 (2021)

    Article  Google Scholar 

  15. Liu, S., Yang, Q., Zhang, S., Wang, T., Xiong, N.N.: MIDP: an MDP-based intelligent big data processing scheme for vehicular edge computing. J. Parallel Distrib. Comput. 167, 1–17 (2022)

    Article  Google Scholar 

  16. Xu, X., Xia, Y., Zeng, F., Li, F., Xie, H., Fu, X., Wang, M.: A novel vehicular task deployment method in hybrid MEC. J. Cloud Comput. 11(1), 88 (2022)

    Article  Google Scholar 

  17. Li, J., et al.: A multi-armed bandits learning-based approach to service caching in edge computing environment. In: Zhang, Y., Zhang, L.J. (eds.) ICWS 2023. LNCS, vol. 14209, pp. 3–17. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-44836-2_1

    Chapter  Google Scholar 

  18. Lakhan, A., Li, X.: Mobility and fault aware adaptive task offloading in heterogeneous mobile cloud environments. In: EAI Endorsed Transactions on Mobile Communications and Applications, vol. 5, no. 16 (2019)

    Google Scholar 

  19. Abd, S.K., Al-Haddad, S.A.R., Hashim, F., Abdullah, A.B., Yussof, S.: Energy-aware fault tolerant task offloading of mobile cloud computing. In: 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), pp. 161–164. IEEE (2017)

    Google Scholar 

  20. Droob, A.: Fault tolerant horizontal computation offloading. In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE), pp. 177–182. IEEE (2023)

    Google Scholar 

  21. Chowdhury, C., Roy, S., Ray, A., Deb, S.K.: A fault-tolerant approach to alleviate failures in offloading systems. Wirel. Pers. Commun. 110(2), 1033–1055 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yunni Xia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, J. et al. (2025). An Evolutionary Game Theoretic-Based Approach to Task Offloading in Hybrid Vehicular Cloud-Edge Environment. In: Zhang, Y., Zhang, LJ. (eds) Web Services – ICWS 2024. ICWS 2024. Lecture Notes in Computer Science, vol 15428. Springer, Cham. https://doi.org/10.1007/978-3-031-77072-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-77072-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77071-5

  • Online ISBN: 978-3-031-77072-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics