Maximal Extractable Value (MEV) searching has gained prominence on the Ethereum blockchain since the surge in Decentralized Finance activities. In Ethereum, MEV extraction primarily hinges on fee payments to block proposers. However, in First-Come-First-Served (FCFS) blockchain networks, the focus shifts to latency optimizations, akin to High-Frequency Trading in Traditional Finance. This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand. We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks and assess its effectiveness. Additionally, our experiments investigate potential optimizations in Algorand's network layer to secure optimal execution positions. Our analysis reveals that while the states of relevant trading pools are updated approximately every six blocks on median, pursuing MEV at the block state level is not viable on Algorand, as arbitrage opportunities are typically executed within the blocks they appear. Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery. Furthermore, our network-level experiments identify critical transaction prioritization strategies for Algorand's FCFS network. Key among these is reducing latency in connections with relays that are well-connected to high-staked proposers.

翻译：最大可提取价值（MEV）搜索自去中心化金融活动激增以来在以太坊区块链上愈发突出。在以太坊中，MEV提取主要依赖于向区块提议者支付费用。然而，在先到先得（FCFS）区块链网络中，焦点转向延迟优化，类似于传统金融中的高频交易。本文阐述了FCFS网络（特别是Algorand）中MEV提取博弈的动态机制。我们提出了一种专为FCFS网络独特时间约束设计的套利检测算法，并评估了其有效性。此外，我们的实验探索了Algorand网络层中确保最佳执行位置的潜在优化方案。分析表明，虽然相关交易池状态中位数约每六个区块更新一次，但在Algorand上从区块状态层面追逐MEV并不可行，因为套利机会通常会在其出现的区块内被执行。我们的算法在不同时间约束下的性能凸显了时序在套利发现中的重要性。此外，我们的网络层实验识别了Algorand的FCFS网络中关键的交易优先级策略，其中最重要的是降低与高质押提议者紧密连接的中继节点的通信延迟。