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Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Wanbo Zhang1, Yuqi Fan1, Jun Zhang1, Xu Ding2,*, Jung Yoon Kim3,*

1 School of Computer Science and Information Engineering, Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, 230601, China
2 Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment, Hefei University of Technology, Hefei, 230009, China
3 College of Future Industry, Gachon University, Seongnam, 13120, South Korea

* Corresponding Authors: Xu Ding. Email: email; Jung Yoon Kim. Email: email

(This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences 2024, 140(1), 863-885. https://doi.org/10.32604/cmes.2024.047295

Abstract

Users and edge servers are not fully mutually trusted in mobile edge computing (MEC), and hence blockchain can be introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in both MEC and blockchain, processing users’ tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users’ offloaded tasks and blockchain tasks simultaneously. Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the MEC is crucial for the processing delay of blockchain-based MEC. Most of the existing research tackles the problem of resource allocation in either blockchain or MEC, which leads to unfavorable performance of the blockchain-based MEC system. In this paper, we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aim to minimize the total system processing delay. For the problem, we propose a computing resource Allocation algorithm for Blockchain-based MEC (ABM) which utilizes the Slater’s condition, Karush-Kuhn-Tucker (KKT) conditions, partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution. Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

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Cite This Article

APA Style
Zhang, W., Fan, Y., Zhang, J., Ding, X., Kim, J.Y. (2024). Computing resource allocation for blockchain-based mobile edge computing. Computer Modeling in Engineering & Sciences, 140(1), 863-885. https://doi.org/10.32604/cmes.2024.047295
Vancouver Style
Zhang W, Fan Y, Zhang J, Ding X, Kim JY. Computing resource allocation for blockchain-based mobile edge computing. Comput Model Eng Sci. 2024;140(1):863-885 https://doi.org/10.32604/cmes.2024.047295
IEEE Style
W. Zhang, Y. Fan, J. Zhang, X. Ding, and J.Y. Kim "Computing Resource Allocation for Blockchain-Based Mobile Edge Computing," Comput. Model. Eng. Sci., vol. 140, no. 1, pp. 863-885. 2024. https://doi.org/10.32604/cmes.2024.047295



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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