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Volume 18, Issue 1
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Background

Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing, edge computing is provided as a new paradigm to effectively reduce processing latency, energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems, an intelligent task migration assignment mechanism based on blockchain is proposed, which jointly considers the factors of resource allocation, resource control and credit degree.

Methods

In this paper, an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay, energy consumption, communication, and credit degree. Here, the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes, and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge.

Results

Compared to the existing two benchmark mechanisms, the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost.

Conclusion

To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.

© 2025 Bentham Science Publishers
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2024-04-15
2025-01-15

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References

  1. EsmatH.H. LorenzoB. ShiW. Toward resilient network slicing for satellite–terrestrial edge computing IoT.IEEE Internet Things J.20231016146211464510.1109/JIOT.2023.3277466
     [Google Scholar]
  2. SunC. LiX. WenJ. WangX. HanZ. LeungV.C.M. Federated deep reinforcement learning for recommendation-enabled edge caching in mobile edge-cloud computing networks.IEEE J. Sel. Areas Comm.202341369070510.1109/JSAC.2023.3235443
     [Google Scholar]
  3. LiY. SunX. XiaY. ChenP. LiY. PengQ. M-MNFT: A novel modified (m, n)-fault tolerance approach for service migration in vehicular edge computingIEEE International Conference on Software Services Engineering (SSE)202317017910.1109/SSE60056.2023.00031
     [Google Scholar]
  4. DongT. ZhangZ. QiuH. ZhangT. LiH. WangT. "Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing", IEEE INFOCOM2023110
     [Google Scholar]
  5. MaX. WangS. ZhangS. YangP. LinC. ShenX. Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing.IEEE Trans. Cloud Comput.20219396898010.1109/TCC.2019.2903240
     [Google Scholar]
  6. ChenS. ZhengY. LuW. VaradarajanV. WangK. Energy optimal dynamic computation offloading for industrial iot in fog computing.IEEE Trans. Green Commun. Netw.20204256657610.1109/TGCN.2019.2960767
     [Google Scholar]
  7. CaiP. YangF. WangJ. WuX. YangY. LuoX. JOTE: Joint offloading of tasks and energy in fog-enabled iot networks.IEEE Internet Things J.2020743067308210.1109/JIOT.2020.2964951
     [Google Scholar]
  8. MinM. XiaoL. ChenY. ChengP. WuD. ZhuangW. Learning-based computation offloading for iot devices with energy harvesting.IEEE Trans. Vehicular Technol.20196821930194110.1109/TVT.2018.2890685
     [Google Scholar]
  9. AliZ. JiaoL. BakerT. AbbasG. AbbasZ.H. KhafS. A deep learning approach for energy efficient computational offloading in mobile edge computing.IEEE Access2019714962314963310.1109/ACCESS.2019.2947053
     [Google Scholar]
  10. GongY. LvC. CaoS. YanL. WangH. Deep learning-based computation offloading with energy and performance optimization.EURASIP J. Wirel. Commun. Netw.2020202016910.1186/s13638‑020‑01678‑5
     [Google Scholar]
  11. NakamotoS. Bitcoin: A Peer to Peer Electronic Cash System.White Paper2008
     [Google Scholar]
  12. TschorschF. ScheuermannB. Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies.IEEE Commun. Surv. Tutor.20161832084212310.1109/COMST.2016.2535718
     [Google Scholar]
  13. EyalI. GencerA.E. SirerE.G. RenesseR.V. "Bitcoin-NG: A Scalable Blockchain Protocol", Symposium on Networked Systems Design and Implementation (USENIX)20164559
     [Google Scholar]
  14. DasD. Toward next generation of blockchain using improvized bitcoin-NG.IEEE Trans. Comput. Soc. Syst.20218251252110.1109/TCSS.2021.3049477
     [Google Scholar]
  15. DehuryC.K. SriramaS.N. DontaP.K. DustdarS. Securing clustered edge intelligence with blockchain.IEEE Consum. Electron. Mag.2024131222910.1109/MCE.2022.3164529
     [Google Scholar]
  16. XuB.H. MingL. Yucai junior high school campus planning and design in qingdao economic and technological development zone.J. Shand.Jianzh. Univ.2014293286292
     [Google Scholar]
  17. MoghariyaJ. ShambharkarP.G. "Blockchain-Enabled IoT (B-IoT): Overview, security, scalability & challenges", International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON)202321021710.1109/TEECCON59234.2023.10335786
     [Google Scholar]
  18. HazraA. AlkhayyatA. AdhikariM. Blockchain for cybersecurity in edge networks.IEEE Consum. Electron. Mag.20241319710210.1109/MCE.2022.3141068
     [Google Scholar]
  19. CaloJ. LoB. "IoT federated blockchain learning at the edge", Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC),202314
     [Google Scholar]
  20. MotlaghS.G. MisicJ. MisicV.B. The impact of selfish mining on bitcoin network performance.IEEE Trans. Netw. Sci. Eng.20218172473510.1109/TNSE.2021.3050034
     [Google Scholar]
  21. XuC. XiongR. ShenX. ZhuL. ZhangX. How to find a bitcoin mixer: a dual ensemble model for bitcoin mixing service detection.IEEE Internet Things J.20231019172201723010.1109/JIOT.2023.3275158
     [Google Scholar]
  22. WangT. BaiX. WangH. LiewS.C. ZhangS. Game-theoretical analysis of mining strategy for bitcoin-ng blockchain protocol.IEEE Syst. J.20211522708271910.1109/JSYST.2020.3004468
     [Google Scholar]
  23. TovanichN. SoulieN. HeulotN. IsenbergP. MiningVis: Visual Analytics of the Bitcoin Mining Economy.IEEE Trans. Vis. Comput. Graph.202228186887810.1109/TVCG.2021.3114821 34596542
     [Google Scholar]
  24. LiZ. YangZ. XieS. ChenW. LiuK. Credit-based payments for fast computing resource trading in edge-assisted internet of things.IEEE Internet Things J.2019646606661710.1109/JIOT.2019.2908861
     [Google Scholar]
/content/journals/rascs/10.2174/0126662558292891240409050246
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A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System
Recent Advances in Computer Science and Communications 18, E150424228934 (2025); https://doi.org/10.2174/0126662558292891240409050246
/content/journals/rascs/10.2174/0126662558292891240409050246
/content/journals/rascs/10.2174/0126662558292891240409050246
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  • Article Type:     Research Article
Keyword(s): blockchain; credit degree; Edge computing; migration model; minimal cost; neural network

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