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Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2023)

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Abstract

Mobile Edge Computing (MEC), an emerging computing paradigm, shifts computing and storage capabilities from the cloud to the network edge, aiming to meet the delay requirements of emerging applications and save backhaul network bandwidth. However, compared to cloud servers, MEC servers have limited computing and storage capabilities, which cannot meet the massive offloading demands of users during high-load periods. In this context, this paper proposes a multi-ENs collaborative task processing model. The model aims to formulate optimal offloading decisions and allocate computing resources for tasks to minimize system delay and cost. To solve this problem, we propose an online algorithm based on Lyapunov optimization called OKMTA, which can work online without the need for predicting future information. Specifically, the problem is formulated as a mixed-integer nonlinear programming (MINLP) problem and decomposed into two subproblems for solution. By using the Lagrange multiplier method to solve the computing resource allocation problem of tasks, and by using matching theory to solve the offloading decision problem of tasks. The simulation results show that our algorithm can achieve near-optimal delay performance while satisfying the long-term system average cost constraint.

The work is supported by the Key Technology Research and Development Project of Hefei, NO. 2021GJ029.

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References

  1. Elgendy, I.A., Zhang, W.-Z., Zeng, Y., He, H., Tian, Y.-C., Yang, Y.: Efficient and secure multi-user multi-task computation offloading for mobile-edge computing in mobile IoT networks. IEEE Trans. Netw. Serv. Manage. 17(4), 2410–2422 (2020). https://doi.org/10.1109/TNSM.2020.3020249

    Article  Google Scholar 

  2. El Haber, E., Nguyen, T.M., Assi, C.: Joint optimization of computational cost and devices energy for task offloading in multi-tier edge-clouds. IEEE Trans. Commun. 67(5), 3407–3421 (2019). https://doi.org/10.1109/TCOMM.2019.2895040

    Article  Google Scholar 

  3. Zhao, M., et al.: Energy-aware task offloading and resource allocation for time-sensitive services in mobile edge computing systems. IEEE Trans. Veh. Technol. 70(10), 10925–10940 (2021). https://doi.org/10.1109/TVT.2021.3108508

    Article  Google Scholar 

  4. Li, Q., Wang, S., Zhou, A., Ma, X., Yang, F., Liu, A.X.: QoS driven task offloading with statistical guarantee in mobile edge computing. IEEE Trans. Mob. Comput. 21(1), 278–290 (2022). https://doi.org/10.1109/TMC.2020.3004225

    Article  Google Scholar 

  5. Chen, Y., Zhang, N., Zhang, Y., Chen, X., Wu, W., Shen, X.S.: TOFFEE: task offloading and frequency scaling for energy efficiency of mobile devices in mobile edge computing. IEEE Trans. Cloud Comput. 9(4), 1634–1644 (2021). https://doi.org/10.1109/TCC.2019.2923692

    Article  Google Scholar 

  6. Zhou, T., Yue, Y., Qin, D., Nie, X., Li, X., Li, C.: Mobile device association and resource allocation in HCNs with mobile edge computing and caching. IEEE Syst. J. 17(1), 976–987 (2023). https://doi.org/10.1109/JSYST.2022.3157590

    Article  Google Scholar 

  7. Ren, J., Yu, G., Cai, Y., He, Y.: Latency optimization for resource allocation in mobile-edge computation offloading. IEEE Trans. Wireless Commun. 17(8), 5506–5519 (2018). https://doi.org/10.1109/TWC.2018.2845360

    Article  Google Scholar 

  8. Chen, Y., Zhang, N., Zhang, Y., Chen, X., Wu, W., Shen, X.: Energy efficient dynamic offloading in mobile edge computing for internet of things. IEEE Trans. Cloud Comput. 9(3), 1050–1060 (2021). https://doi.org/10.1109/TCC.2019.2898657

    Article  Google Scholar 

  9. Ren, J., Yu, G., He, Y., Li, G.Y.: Collaborative cloud and edge computing for latency minimization. IEEE Trans. Veh. Technol. 68(5), 5031–5044 (2019). https://doi.org/10.1109/TVT.2019.2904244

    Article  Google Scholar 

  10. Kai, C., Zhou, H., Yi, Y., Huang, W.: Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability. IEEE Trans. Cogn. Commun. Netw. 7(2), 624–634 (2021). https://doi.org/10.1109/TCCN.2020.3018159

    Article  Google Scholar 

  11. Dai, Y., Xu, D., Maharjan, S., Zhang, Y.: Joint computation offloading and user association in multi-task mobile edge computing. IEEE Trans. Veh. Technol. 67(12), 12313–12325 (2018). https://doi.org/10.1109/TVT.2018.2876804

    Article  Google Scholar 

  12. Xu, X., et al.: Secure service offloading for internet of vehicles in SDN-enabled mobile edge computing. IEEE Trans. Intell. Transp. Syst. 22(6), 3720–3729 (2021). https://doi.org/10.1109/TITS.2020.3034197

    Article  Google Scholar 

  13. Zhou, J., Zhang, X.: Fairness-aware task offloading and resource allocation in cooperative mobile-edge computing. IEEE Internet Things J. 9(5), 3812–3824 (2022). https://doi.org/10.1109/JIOT.2021.3100253

    Article  MathSciNet  Google Scholar 

  14. Zhang, J., Guo, H., Liu, J., Zhang, Y.: Task offloading in vehicular edge computing networks: a load-balancing solution. IEEE Trans. Veh. Technol. 69(2), 2092–2104 (2020). https://doi.org/10.1109/TVT.2019.2959410

    Article  Google Scholar 

  15. Xia, X., et al.: OL-MEDC: an online approach for cost-effective data caching in mobile edge computing systems. IEEE Trans. Mob. Comput. 22(3), 1646–1658 (2023). https://doi.org/10.1109/TMC.2021.3107918

    Article  Google Scholar 

  16. Zhang, F., Han, G., Liu, L., Martinez-Garcia, M., Peng, Y.: Joint optimization of cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. IEEE Internet Things J. 8(18), 14156–14170 (2021). https://doi.org/10.1109/JIOT.2021.3068427

    Article  Google Scholar 

  17. Song, C., Xu, W., Wu, T., Yu, S., Zeng, P., Zhang, N.: QoE-driven edge caching in vehicle networks based on deep reinforcement learning. IEEE Trans. Veh. Technol. 70(6), 5286–5295 (2021). https://doi.org/10.1109/TVT.2021.3077072

    Article  Google Scholar 

  18. Chen, J., Wu, H., Yang, P., Lyu, F., Shen, X.: Cooperative edge caching with location-based and popular contents for vehicular networks. IEEE Trans. Veh. Technol. 69(9), 10291–10305 (2020). https://doi.org/10.1109/TVT.2020.3004720

    Article  Google Scholar 

  19. Gupta, D., Moudgil, A., Wadhwa, S., Solanki, V.: Efficient data caching and computation offloading strategy for edge network. In: 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 1–5 (2022). https://doi.org/10.1109/ESCI53509.2022.9758379

  20. Ren, Q., Abbasi, O., Kurt, G.K., Yanikomeroglu, H., Chen, J.: Caching and computation offloading in high altitude platform station (HAPS) assisted intelligent transportation systems. IEEE Trans. Wireless Commun. 21(11), 9010–9024 (2022). https://doi.org/10.1109/TWC.2022.3171824

    Article  Google Scholar 

  21. Ning, Z., et al.: Intelligent edge computing in internet of vehicles: a joint computation offloading and caching solution. IEEE Trans. Intell. Transp. Syst. 22(4), 2212–2225 (2021). https://doi.org/10.1109/TITS.2020.2997832

    Article  Google Scholar 

  22. Tang, C., Zhu, C., Wu, H., Li, Q., Rodrigues, J.J.: Toward response time minimization considering energy consumption in caching-assisted vehicular edge computing. IEEE Internet Things J. 9(7), 5051–5064 (2022). https://doi.org/10.1109/JIOT.2021.3108902

    Article  Google Scholar 

  23. Xia, X., Chen, F., He, Q., Grundy, J., Abdelrazek, M., Jin, H.: Online collaborative data caching in edge computing. IEEE Trans. Parallel Distrib. Syst. 32(2), 281–294 (2021). https://doi.org/10.1109/TPDS.2020.3016344

    Article  Google Scholar 

  24. Chen, W., Wang, D., Li, K.: Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 12(5), 726–738 (2019). https://doi.org/10.1109/TSC.2018.2826544

    Article  Google Scholar 

  25. Zhao, J., Li, Q., Gong, Y., Zhang, K.: Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans. Veh. Technol. 68(8), 7944–7956 (2019). https://doi.org/10.1109/TVT.2019.2917890

    Article  Google Scholar 

  26. Chen, D., et al.: Matching-theory-based low-latency scheme for multitask federated learning in MEC networks. IEEE Internet Things J. 8(14), 11415–11426 (2021). https://doi.org/10.1109/JIOT.2021.3053283

    Article  Google Scholar 

  27. Wu, H., et al.: Delay-minimized edge caching in heterogeneous vehicular networks: a matching-based approach. IEEE Trans. Wireless Commun. 19(10), 6409–6424 (2020). https://doi.org/10.1109/TWC.2020.3003339

    Article  Google Scholar 

  28. Feng, H., Guo, S., Yang, L., Yang, Y.: Collaborative data caching and computation offloading for multi-service mobile edge computing. IEEE Trans. Veh. Technol. 70(9), 9408–9422 (2021). https://doi.org/10.1109/TVT.2021.3099303

    Article  Google Scholar 

  29. Xu, J., Chen, L., Zhou, P.: Joint service caching and task offloading for mobile edge computing in dense networks. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, USA, pp. 207–215 (2018). https://doi.org/10.1109/INFOCOM.2018.8485977

  30. Zhao, J., Sun, X., Li, Q., Ma, X.: Edge caching and computation management for real-time internet of vehicles: an online and distributed approach. IEEE Trans. Intell. Transp. Syst. 22(4), 2183–2197 (2021). https://doi.org/10.1109/TITS.2020.3012966

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Lei Shi, Shilong Feng, Rui Ji, Juan Xu & Xu Ding

  2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei, 230009, China

    Lei Shi, Shilong Feng, Rui Ji & Juan Xu

  3. Institute of Industry and Equipment Technology, Hefei University of Technology, Hefei, 230009, China

    Xu Ding

  4. Taian Hualu Metalforming Machine Tool Co., Ltd., Taian, 271000, Shandong, China

    Baotong Zhan

Authors
  1. Lei Shi
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  2. Shilong Feng
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  3. Rui Ji
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  4. Juan Xu
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  5. Xu Ding
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Correspondence to Shilong Feng .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool, Suzhou, China

    Xinheng Wang

  3. University of Peloponnese, Patra, Greece

    Nikolaos Voros

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Shi, L., Feng, S., Ji, R., Xu, J., Ding, X., Zhan, B. (2024). Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-54521-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-54521-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54520-7

  • Online ISBN: 978-3-031-54521-4

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