基于数据驱动模型的以太坊验证器困境分析
Data-Driven Model-Based Analysis of the Ethereum Verifier's Dilemma
摘要：
在以太坊等基于工作量证明的区块链中，区块验证是建立跨节点共识的一个组成部分。然而，在以太坊中，矿工不会因为验证而获得奖励。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区块开采？我们提供了一个广泛的分析验证者的困境，使用数据驱动的基于模型的方法，结合封闭形式的表达式，机器学习技术和离散事件模拟。我们从超过30万个智能合约中收集数据，并通过实验获得它们的CPU执行时间。高斯混合模型和随机森林回归将数据转换为适合模拟器的分布和输入。我们表明，事实上，它往往是经济上合理的不核实。我们考虑两种方法来减轻验证者的困境，即并行化和主动插入无效块的影响，这两种方法都将被证明是有效的。
英文摘要：
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.
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