论文标题
基于数据驱动的模型分析以太坊验证者的困境
Data-Driven Model-Based Analysis of the Ethereum Verifier's Dilemma
论文作者
论文摘要
在基于工作证明的区块链（例如以太坊）中，对区块的验证是跨节点建立共识的组成部分。但是，在以太坊中，矿工没有获得验证的奖励。这意味着矿工面临验证者的困境：使用资源进行验证，还是将其用于更有利可图的新块挖掘？我们使用基于数据驱动的模型的方法对验证者的困境进行了广泛的分析，该方法结合了封闭形式表达式，机器学习技术和离散事件模拟。我们从300,000多个智能合约中收集数据，并通过实验获得其CPU执行时间。高斯混合物模型和随机森林回归将数据转化为适合模拟器的分布和输入。我们表明，实际上，在经济上通常不验证是合理的。我们考虑两种方法来减轻验证者困境的含义，即并行化和无效块的主动插入，这两个方法都将证明是有效的。
In proof-of-work based blockchains such as Ethereum, verification of blocks is an integral part of establishing consensus across nodes. However, in Ethereum, miners do not receive a reward for verifying. This implies that miners face the Verifier's Dilemma: use resources for verification, or use them for the more lucrative mining of new blocks? We provide an extensive analysis of the Verifier's Dilemma, using a data-driven model-based approach that combines closed-form expressions, machine learning techniques and discrete-event simulation. We collect data from over 300,000 smart contracts and experimentally obtain their CPU execution times. Gaussian Mixture Models and Random Forest Regression transform the data into distributions and inputs suitable for the simulator. We show that, indeed, it is often economically rational not to verify. We consider two approaches to mitigate the implications of the Verifier's Dilemma, namely parallelization and active insertion of invalid blocks, both will be shown to be effective.
