Resource Matching for Blockchain-Assisted Edge Computing Networks

The combination of edge computing (EC) and blockchain can enhance task processing while ensuring security and credibility. To maximize system performance and avoid resource waste in task offloading, it is essential to match the resource allocation of the task computing and the blockchain consensus p...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-04, Vol.11 (8), p.14460-14471
Main Authors: Fan, Wenhao, Hao, Zhibo, Tang, Bihua, Wu, Fan, Liu, Yuan'an
Format: Article
Language:English
Subjects:
Algorithms
Blockchain
Blockchains
Cloud computing
Computation offloading
Cryptography
Delay
Delays
Edge computing
edge computing (EC)
Energy consumption
Machine learning
Matching
Numerical methods
Optimization
Power control
Radio equipment
Resource allocation
Resource management
Servers
Task analysis
task offloading
Task scheduling
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Summary:The combination of edge computing (EC) and blockchain can enhance task processing while ensuring security and credibility. To maximize system performance and avoid resource waste in task offloading, it is essential to match the resource allocation of the task computing and the blockchain consensus process. However, the existing works treated the above two processes as two independent processes and optimized them separately and ignored the above matching problem. In this article, we propose a resource management scheme for blockchain-assisted EC networks consisting of multiple devices, multiple base stations equipped with edge servers, a cloud server, and a network controller deployed on the edge layer. To minimize the total task processing delay and energy consumption of the devices, we formulate a joint task processing problem incorporating task scheduling, transmit power control, and computing resource allocation. To match the computing delay and consensus delay of each task, we balance the computing resources allocated for the two processes. We design a deep reinforcement learning (DRL) algorithm that utilizes the twin-delayed deep deterministic policy gradient (TD3) technology embedded with a fast numerical method, which effectively reduces the training complexity of the DRL model. Extensive experiments are conducted by varying four crucial parameters. The superiority of our scheme is demonstrated in comparison with three other reference schemes. The performance of our scheme is about 18.3%-24.1% higher than that of other schemes.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3342439