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Edge bank: a novel resource pricing and management system for edge service provider

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Abstract

In order to address the challenges of digital transformation in data-driven enterprises resulting from edge computing, an efficient resource pricing strategy for edge service providers (ESPs) is essential. This paper introduces a novel resource pricing and management system for ESPs, termed the "edge bank," which models edge nodes in resource pricing as borrowers and lenders analogous to a commercial bank. We investigate two key issues within this edge bank system: (1) the operational mechanism of the edge bank, and (2) the resource pricing and management strategy employed by the edge bank for ESPs. To address the first issue, we present an implementation framework for resource pricing and management within the edge bank system. For the second issue, we propose a two-stage resource pricing strategy based on game theory, tailored to the system's characteristics, which consists of an initial stage and a steady stage. Additionally, we develop a price adjustment algorithm that optimizes the loan-to-deposit ratio (LDR) in the edge bank system. Numerical results demonstrate the effectiveness of the two-stage pricing strategy and highlight the superior performances of proposed algorithm to enhance resource utilization for ESPs.

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References

  1. Muller JM, Buliga O, Voigt K-I (2021) The role of absorptive capacity and innovation strategy in the design of industry 40 business models - a comparison between SMEs and large enterprises. Eur Manag J 39(3):333–343. https://doi.org/10.1016/j.emj.2020.01.002

    Article  Google Scholar 

  2. Ahmed I, Zhang Y, Jeon G, Lin W, Khosravi MR, Qi L (2022) A blockchain-and artificial intelligence-enabled smart IoT framework for sustainable city. Int J Intell Syst. https://doi.org/10.1002/int.22852

    Article  Google Scholar 

  3. Li S, Liu H, Li W, Sun W (2022) An optimization framework for migrating and deploying multiclass enterprise applications into the cloud. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2022.3174216

    Article  Google Scholar 

  4. Lin C, Li Y, Ahmed M, Song C (2023) Piece-wise pricing optimization with computation resource constraints for parked vehicle edge computing. Peer-to-Peer Netw Appl 16(2):709–726. https://doi.org/10.1007/s12083-022-01427-z

    Article  Google Scholar 

  5. Fan Y, Jin Z, Shen G, Hu D, Shi L, Yuan X (2021) Three-stage Stackelberg game based edge computing resource management for mobile blockchain. Peer-to-Peer Netw Appl 14(3):1431–1445. https://doi.org/10.1007/s12083-020-01032-y. (Accessed 2022-06-30)

    Article  Google Scholar 

  6. Wang Y, Chen C-R, Huang P-Q, Wang K (2021) A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network. Comput Ind Eng 155:107186. https://doi.org/10.1016/j.cie.2021.107186

    Article  Google Scholar 

  7. Fan Y, Wang L, Wu W, Du D (2021) Cloud/edge computing resource allocation and pricing for mobile blockchain: an iterative Greedy and Search approach. IEEE Trans Comput Soc Syst 8(2):451–463. https://doi.org/10.1109/TCSS.2021.3049152. (Accessed 2022-06-30)

    Article  Google Scholar 

  8. Huang C-F, Huang D-H, Lin Y-K (2020) Network reliability evaluation for a distributed network with edge computing. Comput Ind Eng 147:106492. https://doi.org/10.1016/j.cie.2020.106492

    Article  Google Scholar 

  9. Karupusamy S, Refonaa J, Sankaran S, Dahiya P, Haq MA, Kumar A (2023) Effective energy usage and data compression approach using data mining algorithms for IoT data. Expert Syst 40(4):12997. https://doi.org/10.1111/exsy.12997

    Article  Google Scholar 

  10. Wang J, Zhao L, Liu J, Kato N (2021) Smart resource allocation for mobile edge computing: a deep reinforcement learning approach. IEEE Trans Emerg Topics Comput 9(3):1529–1541. https://doi.org/10.1109/TETC.2019.2902661. (Accessed 2022-11-06)

    Article  Google Scholar 

  11. Shakarami A, Shakarami H, Ghobaei-Arani M, Nikougoftar E, Faraji-Mehmandar M (2022) Resource provisioning in edge/fog computing: a comprehensive and systematic review. J Syst Archit 122:102362. https://doi.org/10.1016/j.sysarc.2021.102362

    Article  Google Scholar 

  12. Baek B, Lee J, Peng Y, Park S (2020) Three dynamic pricing schemes for resource allocation of edge computing for IoT environment. IEEE Internet Things J 7(5):4292–4303. https://doi.org/10.1109/JIOT.2020.2966627

    Article  Google Scholar 

  13. Sun W, Liu J, Yue Y, Zhang H (2018) Double auction-based resource allocation for mobile edge computing in industrial Internet of Things. IEEE Trans Ind Inform 14(10):4692–4701. https://doi.org/10.1109/TII.2018.2855746

    Article  Google Scholar 

  14. Baranwal G, Kumar D, Vidyarthi DP (2022) BARA: a blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things. Future Gener Comput Syst 135:333–347. https://doi.org/10.1016/j.future.2022.05.007

    Article  Google Scholar 

  15. Lv H, Zheng Z, Wu F, Chen G (2021) Strategy-proof online mechanisms for weighted AoI minimization in edge computing. IEEE J Sel Areas Commun 39(5):1277–1292. https://doi.org/10.1109/JSAC.2021.3065078

    Article  Google Scholar 

  16. Lin X, Wu J, Mumtaz S, Garg S, Li J, Guizani M (2021) Blockchain-based on-demand computing resource trading in IoV-assisted smart city. IEEE Trans Emerg Topics Comput 9(3):1373–1385. https://doi.org/10.1109/TETC.2020.2971831

    Article  Google Scholar 

  17. Lin, R., Xu, H., Li, M., Zhang, Z.: Resource allocation in edge-computing based wireless networks based on differential game and feedback control. Comput Mater Contin 64(2), 961–972 (2020) https://doi.org/10.32604/cmc.2020.09686

  18. Huang X, Zhang W, Yang J, Yang L, Yeo CK (2021) Market-based dynamic resource allocation in mobile edge computing systems with multi-server and multi-user. Comput Commun 165:43–52. https://doi.org/10.1016/j.comcom.2020.11.001

    Article  Google Scholar 

  19. Jie Y, Tang X, Choo K-KR, Su S, Li M, Guo C (2018) Online task scheduling for edge computing based on repeated Stackelberg game. J Parallel Distrib Comput 122:159–172. https://doi.org/10.1016/j.jpdc.2018.07.019

    Article  Google Scholar 

  20. Wang T, Lu Y, Wang J, Dai H-N, Zheng X, Jia W (2021) EIHDP: edge-intelligent hierarchical dynamic pricing based on cloud-edge-client collaboration for IoT systems. IEEE Trans Comput 70(8):1285–1298. https://doi.org/10.1109/TC.2021.3060484

    Article  MathSciNet  Google Scholar 

  21. Li Z, Kang J, Yu R, Ye D, Deng Q, Zhang Y (2018) Consortium blockchain for secure energy trading in industrial Internet of Things. IEEE Trans Ind Inform 14(8):3690–3700. https://doi.org/10.1109/TII.2017.2786307

    Article  Google Scholar 

  22. Siew, M., Cai, D., Li, L., Quek, T.Q.S.: A Sharing-Economy Inspired Pricing Mechanism for Multi-Access Edge Computing. In: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, pp. 1–6. IEEE, Taipei, Taiwan (2020). https://doi.org/10.1109/GLOBECOM42002.2020.9322554. Accessed 2022-06-30

  23. Zhang Y, Lan X, Ren J, Cai L (2020) Efficient computing resource sharing for mobile edge-cloud computing networks. IEEE/ACM Trans Netw 28(3):1227–1240. https://doi.org/10.1109/TNET.2020.2979807

    Article  Google Scholar 

  24. Nguyen DT, Le LB, Bhargava VK (2019) A market-based framework for multi-resource allocation in fog computing. IEEE/ACM Trans Netw 27(3):1151–1164. https://doi.org/10.1109/TNET.2019.2912077

    Article  Google Scholar 

  25. Xu H, Qiu X, Zhang W, Liu K, Liu S, Chen W (2021) Privacy-preserving incentive mechanism for multi-leader multi-follower IoT-edge computing market: a reinforcement learning approach. J Syst Archit 114:101932. https://doi.org/10.1016/j.sysarc.2020.101932

    Article  Google Scholar 

  26. Zhang, F., Tang, Z., Chen, M., Zhou, X., Jia, W.: A Dynamic Resource Overbooking Mechanism in Fog Computing. In: 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 89–97. IEEE, Chengdu (2018). https://doi.org/10.1109/MASS.2018.00023. https://ieeexplore.ieee.org/document/8567545/ Accessed 2022-07-01

  27. Huang X, Gong S, Yang J, Zhang W, Yang L, Yeo CK (2022) Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information. Futur Gener Comput Syst 127:80–91. https://doi.org/10.1016/j.future.2021.08.029

    Article  Google Scholar 

  28. Bao Y, Qiu W, Cheng X (2023) Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices. Expert Syst 40(6):13220. https://doi.org/10.1111/exsy.13220

    Article  Google Scholar 

  29. Wang X, Ni D (2023) Internet based rural economic entrepreneurship based on mobile edge computing and resource allocation. Soft Comput. https://doi.org/10.1007/s00500-023-08620-z

    Article  Google Scholar 

  30. Khorasani N, Abrishami S, Feizi M, Esfahani MA, Ramezani F (2020) Resource management in the federated cloud environment using Cournot and Bertrand competitions. Future Gener Comput Syst 113:391–406. https://doi.org/10.1016/j.future.2020.07.010

    Article  Google Scholar 

  31. Assila B, Kobbane A, Ben-Othman J (2020) Improving caching resource management: A pricing economic approach using Cournot, Bertrand, and Stackelberg game models Managing caching resource in 5G mobile networks. Int J Commun Syst. https://doi.org/10.1002/dac.4358

    Article  Google Scholar 

  32. Assila, B., Kobbane, A., El Koutbi, M.: A Cournot Economic Pricing Model for Caching Resource Management in 5G Wireless Networks. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1345–1350. IEEE, Limassol, Cyprus (2018). https://doi.org/10.1109/IWCMC.2018.8450538. https://ieeexplore.ieee.org/document/8450538/ Accessed 2022-04-16

  33. Zheng Z, Song L, Han Z, Li GY, Poor HV (2018) A Stackelberg game approach to proactive caching in large-scale mobile edge networks. IEEE Trans Wirel Commun 17(8):5198–5211. https://doi.org/10.1109/TWC.2018.2839111

    Article  Google Scholar 

  34. Dia E, VanHoose D (2019) Real resource utilization in banking, economies of scope, and the relationship between retail loans and deposits. Econ Lett 177:39–42. https://doi.org/10.1016/j.econlet.2019.01.018

    Article  Google Scholar 

  35. End JW (2013) A macroprudential approach to address liquidity risk with the loan-to-deposit ratio. SSRN Electron J. https://doi.org/10.2139/ssrn.2228599

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the support from the National Natural Science Foundation of China (No. 71971188), the Humanity and Social Science Foundation of Ministry of Education of China (No. 22YJCZH086), the Hebei Natural Science Foundation (Nos. G2022203003, G2023203008) and the support Funded by Science and Technology Project of Hebei Education Department (No. ZD2022142).

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Shiyong Li: Conceptualization, Supervision. Huan Liu: Writing—original draft. Wenzhe Li: Investigation, Visualization. Wei Sun: Funding acquisition, Writing—Review & Editing.

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Correspondence to Huan Liu.

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Li, S., Liu, H., Li, W. et al. Edge bank: a novel resource pricing and management system for edge service provider. J Supercomput 81, 278 (2025). https://doi.org/10.1007/s11227-024-06578-9

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