Performance Analyses for Applying Machine Learning on Bitcoin Miners
Performance Analyses for Applying Machine Learning on Bitcoin Miners
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在比特币矿工上应用机器学习的性能分析
DOI：
10.1109/iceic51217.2021.9369776
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sang
中科院分区：
文献类型：
--
作者：
Wenjun Fan;Jinoh Kim;Ikkyun Kim;Xiaobo Zhou;Sang
比特币和加密货币依赖于点对点（P2P）网络。收集比特币矿工的情报来分析和控制网络，可以改善信息传递并抵御网络威胁。然而，将机器学习（ML）应用于构建智能带来了挑战，因为矿工参与资源密集型分布式共识协议，ML应用程序可能会消耗大量计算资源。在本文中，我们研究了ML算法和挖掘操作之间的可行性和相互作用。我们基于原型的实验测量和比较ML算法的性能，以评估ML算法的实现开销和效率及其对挖掘操作的影响，即，当ML算法并行运行时，挖掘减少。
Bitcoin and cryptocurrency rely on peer-to-peer (P2P) networking. Incorporating intelligence on Bitcoin miners to analyze and control networking can improve the information delivery and defend against networking threats. However, applying machine learning (ML) for building intelligence introduces a challenge because miners participate in the resource-intensive distributed consensus protocol and the ML application can consume much computing resources. In this paper, we study the feasibility and the interplay between ML algorithms and mining operations. Our prototype-based experiments measure and compare the performance of the ML algorithms to evaluate the implementation overhead and efficiencies of the ML algorithms and their impacts on mining operations, i.e., the mining reduction when the ML algorithm is running in parallel.