Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.

翻译：在基于工作量证明的区块链中，挖矿已成为一项昂贵的活动，需要能够每秒执行数兆哈希的专用硬件，并耗费巨大的电力成本。矿工每次在最长链上挖掘一个区块时，会获得奖励，这有助于抵消其挖矿成本。因此，矿工希望最大化其在区块链中挖掘的区块数量以增加收入。影响挖矿奖励的一个关键因素是矿工间点对点网络的连通性。为最大化奖励，矿工必须谨慎选择其网络连接，确保存在通往其他矿工的路径，且这些路径的平均延迟低于其他矿工之间的路径。我们将矿工决定连接对象的问题表述为一个组合赌博机问题。每个节点策略性地选择其邻居，以最小化到达网络90%算力的延迟，相对于其他节点90%分位延迟。我们工作的一个关键贡献是在赌博机算法中使用基于网络坐标的模型来学习网络结构。实验表明，我们提出的算法在不同网络设置中优于或持平于基线方法。