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Dynamic game based task offloading and resource pricing in LEO-multi-access edge computing

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Abstract

Driven by the demand for ubiquitous connection in the Internet of everything era, this paper introduces a new low earth orbit (LEO) satellite-based multi-access edge computing fusion architecture. The architecture regards LEO satellites deployed with high-performance edge modules as superior nodes, which can provide onboard processing mode edge task offloading service for users in complex regions. At the same time, the superior node also has the offloading decision and can offload some edge user tasks to the edge service center in the ground network. Based on differential game theory, we propose a two-stage computing resource purchase strategy and task-offloading resource pricing strategy to ensure the minimum cost of edge computing services for edge users and the maximum benefit for edge service providers and prove the existence of a unique Nash equilibrium solution using Piccard theorem. An algorithm based on the Runge–Kutta method is designed to solve Nash equilibrium, and the simulation results show the effectiveness of the proposed method.

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References

  1. Cisco Annual Internet Report. [Online]. https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/index.html

  2. Shi W, Cao J, Zhang Q et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  3. Dowhuszko A A, Fraire J, Shaat M et al (2020) LEO satellite constellations to offload optical terrestrial networks in placement of popular content in 5G edge nodes. In: 2020 22nd international conference on transparent optical networks (ICTON). IEEE, pp 1–6

  4. Hong Z, Chen W, Huang H et al (2019) Multi-hop cooperative computation offloading for industrial IoT-edge-cloud computing environments. IEEE Trans Parallel Distrib Syst 30(12):2759–2774

    Article  Google Scholar 

  5. Streaming Subscription Mooching in (2022) [Online]. https://cordcutting.com/research/subscription-mooching/

  6. Cong R, Zhao Z, Zhang L, Min G (2022) CoopEdge: cost-effective server deployment for cooperative multi-access edge computing. In: 2022 19th annual IEEE international conference on sensing, communication, and networking (SECON), Stockholm, Sweden, pp 208–216. https://doi.org/10.1109/SECON55815.2022.9918592

  7. Jin H, Luo R, He Q, Wu S, Zeng Z, Xia X (2023) Cost-effective data placement in edge storage systems with erasure code. IEEE Trans Serv Comput 16(2):1039–1050. https://doi.org/10.1109/TSC.2022.3152849

    Article  Google Scholar 

  8. Lai P et al (2022) Cost-effective app user allocation in an edge computing environment. IEEE Trans Cloud Comput 10(3):1701–1713. https://doi.org/10.1109/TCC.2020.3001570

    Article  MathSciNet  Google Scholar 

  9. Tout H, Mourad A, Kara N, Talhi C (2021) Multi-persona mobility: joint cost-effective and resource-aware mobile-edge computation offloading. IEEE/ACM Trans Netw 29(3):1408–1421. https://doi.org/10.1109/TNET.2021.3066558

    Article  Google Scholar 

  10. Zhou Z, Yu S, Chen W, Chen X (2020) CE-IoT: cost-effective cloud-edge resource provisioning for heterogeneous IoT applications. IEEE Internet Things J 7(9):8600–8614. https://doi.org/10.1109/JIOT.2020.2994308

    Article  Google Scholar 

  11. Du J, Sun Y, Zhang N, Xiong Z, Sun A, Ding Z (2023) Cost-effective task offloading in NOMA-enabled vehicular mobile edge computing. IEEE Syst J 17(1):928–939. https://doi.org/10.1109/JSYST.2022.3167901

    Article  Google Scholar 

  12. Xu F, Yang F, Zhao C et al (2020) Deep reinforcement learning based joint edge resource management in maritime network. China Commun 17(5):211–222

    Article  Google Scholar 

  13. Qiu C, Yao H, Yu FR et al (2019) Deep q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks. IEEE Trans Veh Technol 68(6):5871–5883

    Article  Google Scholar 

  14. Zhang Z, Zhang W, Tseng FH (2019) Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques. IEEE Netw 33(1):70–76

    Article  Google Scholar 

  15. Wang Y, Zhang J, Zhang X et al (2018) A computation offloading strategy in satellite terrestrial networks with double edge computing. In: 2018 IEEE international conference on communication systems (ICCS). IEEE, pp 450–455

  16. Tang Q, Fei Z, Li B et al (2021) Computation offloading in Leo satellite networks with hybrid cloud and edge computing. IEEE Internet Things J 8(11):9164–9176

    Article  Google Scholar 

  17. Siew M, Cai D, Li L et al (2020) Dynamic pricing for resource-quota sharing in multi-access edge computing. IEEE Trans Netw Sci Eng 7(4):2901–2912

    Article  MathSciNet  Google Scholar 

  18. Baek B, Lee J, Peng Y et al (2020) Three dynamic pricing schemes for resource allocation of edge computing for IoT environment. IEEE Internet Things J 7(5):4292–4303

    Article  Google Scholar 

  19. Yan J, Bi S, Duan L et al (2021) Pricing-driven service caching and task offloading in mobile edge computing. IEEE Trans Wirel Commun 20(7):4495–4512

    Article  Google Scholar 

  20. Mukherjee M, Kumar V, Zhang Q et al (2021) Optimal pricing for offloaded hard-and soft-deadline tasks in edge computing. IEEE Trans Intell Transp Syst 23:9829–9839

    Article  Google Scholar 

  21. Chang ZL, Wang CY, Wei HY (2021) Flat-rate pricing and truthful offloading mechanism in multi-layer edge computing. IEEE Trans Wirel Commun 20(9):6107–6121

    Article  Google Scholar 

  22. Onori S, Serrao L, Rizzoni G et al (2016) Pontryagin's minimum principle [J]. Hybrid Electr Veh: Energy Manage Strat 51–63

  23. Xu H, Huang W, Zhou Y et al (2021) Edge computing resource allocation for unmanned aerial vehicle assisted mobile network with blockchain applications [J]. IEEE Trans Wirel Commun 20(5):3107–3121. https://doi.org/10.1109/TWC.2020.3047496

  24. Xu H, Guo C, Zhang L (2017) Optimal power control in wireless powered sensor networks: a dynamic game-based approach. Sensors 17:547

    Article  Google Scholar 

  25. Song Z, Hao Y, Liu Y et al (2021) Energy-efficient multiaccess edge computing for terrestrial-satellite internet of things. IEEE Internet Things J 8(18):14202–14218

    Article  Google Scholar 

  26. Dey B, Bhattacharyya B, Devarapalli R (2021) A novel hybrid algorithm for solving emerging electricity market pricing problem of microgrid. Int J Intell Syst 36(2):919–961

    Article  Google Scholar 

  27. Schlage-Puchta JC (2021) Optimal version of the Picard–Lindelöf theorem. Electron J Qual Theory Differ Equ 39:1–8

    Article  Google Scholar 

  28. Hernández-Solano Y, Atencia M (2022) Numerical methods that preserve a Lyapunov function for ordinary differential equations. arXiv preprint arXiv:2204.11225

  29. Wood L, Lou Y, Olusola O (2014) Revisiting elliptical satellite orbits to enhance the O3b constellation. Physics 67(3):110–118

    Google Scholar 

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, No.30 of XueYuan Road, Beijing, 100083, Beijing, People’s Republic of China

    Haoyu Wang

  2. School of Computer and Communication Engineering, University of Science and Technology Beijing, No.30 of XueYuan Road, Beijing, 100083, Beijing, People’s Republic of China

    Jianwei An

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  1. Haoyu Wang
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Haoyu Wang wrote the main manuscript text and Jianwei An prepared Figs. 1 and 2. All authors reviewed the manuscipt.

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Wang, H., An, J. Dynamic game based task offloading and resource pricing in LEO-multi-access edge computing. Computing 106, 579–606 (2024). https://doi.org/10.1007/s00607-023-01234-1

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