Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach (1711.02844v2)

Abstract: Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the data training to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the Edge Computing Service Provider. We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue

• The paper's main contribution is a deep learning-based auction model that optimizes edge computing resource allocation for mobile blockchain networks.
• The methodology integrates Myerson's auction theory with a multi-layer neural network to achieve higher revenue than traditional second-price auctions.
• Simulation results demonstrate that the proposed model efficiently balances resource allocation, enhancing performance and profitability for service providers.

Summary of "Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach"

The paper by Nguyen Cong Luong et al. presents a comprehensive exploration into edge computing resource management for mobile blockchain networks, utilizing an auction mechanism optimized via deep learning. The necessity of this research stems from the high computational and energy requirements for blockchain mining activities, which are not well-suited to the capabilities of mobile devices. To mitigate these constraints, the edge computing paradigm is adopted, proposing a mechanism to allocate computational resources effectively.

Key Contributions

The main contribution of the paper is the development of a deep learning-based optimal auction model designed to manage edge computing resources dynamically. The proposed model derives its foundation from the analytical solutions in auction theory, aiming to maximize the revenue of the Edge Computing Service Provider (ECSP) while ensuring properties such as incentive compatibility and individual rationality.

Methodology

1. Auction Design: The paper proposes an auction mechanism that employs a multi-layer neural network. The design is aligned with the theoretical constructs of Myerson's optimal auction framework. The neural network performs transformations of miners' bids and applies allocation and payment rules.
2. Training Process: The neural network is trained using valuation profiles from miners, drawn from predetermined statistical distributions. This training process focuses on minimizing the expected, negated revenue function, aiming to optimize the revenue outcome for the ECSP.
3. Implementation and Simulation: Simulation outcomes convey the capability of the deep learning mechanism to converge to solutions yielding significantly higher revenue than traditional auction methods like the Second-Price Auction (SPA). This is a pivotal result, exhibiting the practical utility and potential economic benefits of adopting sophisticated machine learning techniques in complex market mechanisms.

Results and Implications

The experimental results demonstrate that deep learning facilitates enhanced revenue generation over conventional auction methods. This finding underlines the potential of integrating advanced computational models into the management of mobile blockchain networks. Additionally, the paper reveals influences of various parameters, like the number of participating miners and the initial capacity distribution, on the auction's efficiency and revenue outcomes. Such insights are invaluable for ECSPs aiming to optimize their resource allocation strategies.

Future Directions

The paper suggests expanding the research framework to consider scenarios involving multiple resource units, increasing the model's applicability to real-world blockchain environments. Furthermore, it indicates the need for developing neural network architectures that can derive optimal auction mechanisms independently from established analytical solutions. Both directions promise to offer substantial advancements in the application of machine learning to economics and network management.

In conclusion, the research by Luong et al. represents a significant step forward in the utilization of deep learning techniques for resource management in mobile blockchain networks. The proposed auction mechanism not only enhances ECSP revenue but also maintains the theoretical rigor required for incentive-compatible and rational participation, offering a robust tool for future implementation in distributed computing environments.