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Trust-Enabled Decentralized Task Offloading for Collaborative Edge Computing Using Blockchain and Deep Reinforcement Learning
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AI
DOI：10.1002/spe.70032.png)
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Abstract
En 中文
Objective Collaborative edge computing (CEC) addresses the service quality issues that arise from the limited resources of a single node in traditional edge computing architectures by integrating resources from multiple edge nodes. However, ensuring reliable task offloading in this collaborative environment remains a significant challenge. Existing solutions often struggle to balance the intelligence and trustworthiness of offloading decisions effectively. This imbalance can lead to poor performance and reduced task success rates, especially if tasks are offloaded to malicious nodes.Methods To tackle these challenges, this paper proposes a trust-enabled decentralized task offloading scheme that combines blockchain technology and deep reinforcement learning (DRL). First, we introduce a blockchain-based reputation mechanism within the CEC architecture to facilitate trusted collaboration among nodes, utilizing smart contracts for reputation management. Next, we propose a beta distribution-based three-factor reputation update (BTRU) algorithm to enhance the accuracy of reputation evaluation. Finally, we present a decentralized and trust-enabled task offloading (DTTO) algorithm based on DRL, which uses on-chain reputation data to guide agents in learning trustworthy task offloading policies, thereby maximizing offloading trustworthiness and task success rates.Result To thoroughly assess the effectiveness and practicality of our proposed scheme, we develop a testbed for CEC task offloading based on Kubernetes and Ethereum. Experimental results demonstrate that the BTRU algorithm effectively distinguishes malicious nodes, reducing their average reputation by 97.54%, with an improvement of 9.94% compared to competitive algorithms. Meanwhile, the DTTO algorithm significantly enhances the efficiency and reliability of task offloading, raising the task success rate by at least 3.04%, especially when the proportion of malicious nodes reaches 40%, its task success rate is at least 5.41% higher than that of competitive algorithms.Conclusion The proposed trust-enabled decentralized task offloading scheme successfully combines blockchain-based reputation management with DRL to achieve both intelligent and trustworthy task offloading in the CEC environments. The experimental validation confirms the scheme's effectiveness in identifying malicious nodes and improving task success rates under various system conditions.
Keywords:
blockchain
collaborative edge computing
decentralized task offloading
deep reinforcement learning
reputation management
Journal
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IF:
2.7
Papers:
27
Citations:
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