Fog computing is considered to be a key enabling technology for future networks. By broadening the cloud computing services to the network edge, fog computing can support various emerging applications such as IoT, big data, and blockchain with low latency and low bandwidth consumption cost. To achieve the full potential of fog computing, it is essential to design an incentive mechanism for fog computing service providers. Auction is a promising solution for the incentive mechanism design. However, it is challenging to design an optimal auction that maximizes the revenue for the providers while holding important properties: IR and IC. Therefore, this article introduces the design of an optimal auction based on deep learning for the resource allocation in fog computing. The proposed optimal auction is developed specifically to support blockchain applications. In particular, we first discuss resource management issues in fog computing. Second, we review economic and pricing models for resource management in fog computing. Third, we introduce fog computing and blockchain. Fourth, we present how to design the optimal auction by using deep learning for the fog resource allocation in the blockchain network. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme (i.e., the greedy algorithm) in terms of revenue, and IC and IR violations. Thus, the proposed scheme can be used as a useful tool for the optimal resource allocation in general fog networks.

中文翻译：

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雾计算中基于机器学习的资源交易拍卖


雾计算被认为是未来网络的关键使能技术。通过将云计算服务扩展到网络边缘，雾计算可以以低延迟和低带宽消耗成本支持物联网、大数据、区块链等各种新兴应用。为了充分发挥雾计算的潜力，有必要为雾计算服务提供商设计激励机制。拍卖是一种很有前景的激励机制设计解决方案。然而，设计一个最佳拍卖，在保留重要财产（IR 和 IC）的同时最大化提供商的收入是一项挑战。因此，本文介绍了一种基于深度学习的最优拍卖设计，用于雾计算中的资源分配。拟议的最佳拍卖是专门为支持区块链应用程序而开发的。特别是，我们首先讨论雾计算中的资源管理问题。其次，我们回顾了雾计算中资源管理的经济和定价模型。第三，我们介绍雾计算和区块链。第四，我们介绍了如何利用深度学习在区块链网络中进行雾资源分配来设计最优拍卖。仿真结果表明，所提出的方案在收入、IC 和 IR 违规方面优于基线方案（即贪婪算法）。因此，所提出的方案可以用作一般雾网络中优化资源分配的有用工具。