We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.

中文翻译：

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基于学习的移动边缘计算资源管理以支持公共区块链网络

我们考虑在移动边缘计算 (MEC) 网络中实现的公共区块链，其中区块链矿工相互竞争以解决工作量证明难题并赢得采矿奖励。由于其移动终端的计算能力有限，矿工将计算卸载到 MEC 服务器。MEC 服务器由向矿工出售其计算资源的服务提供商 (SP) 维护。SP 旨在在受矿工预算限制的情况下最大化其长期利润。矿工同时独立地决定他们的哈希率，即计算能力，以最大化他们的收益，而不会将他们的决定透露给其他矿工。因此，SP 和矿工之间的交互被建模为私有信息下的随机 Stackelberg 博弈，其中 SP 分配每单位哈希率的价格，矿工选择他们的行动，即哈希率决策，而不观察其他矿工的行动。我们基于 SP 和矿工决策过程的完全和部分可观察马尔可夫决策模型为该游戏开发了一个分层学习框架。我们表明，所提出的学习算法收敛到稳定状态，在这种状态下，矿工的行为是对 SP 指定的最优价格的最佳响应。