Playing the MEV Game on a First-Come-First-Served Blockchain

TL;DR

This work addresses latent MEV dynamics on FCFS blockchains by analyzing Algorand through a two-stage lens: a cyclic-arbitrage discovery algorithm operating on block state data and a latency-driven extraction phase examined via private-network experiments. It demonstrates that arbitrage opportunities are largely closed within the originating block and that execution hinges on ultra-low latency paths and relay/topology choices, rather than transaction fees. The study contributes a concrete cyclic-arbitrage detection framework, empirical findings on time-window effects ($\tau$), and network-level insights that emphasize prioritizing connections to relays linked to high-staked proposers. The results inform design and operational strategies for FCFS MEV and provide practical guidance for latency-aware network optimization, with future work aimed at mempool-level detection and mainnet validation.

Abstract

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.

Figures (5)

• Figure 1: Time series of arbitrage revenue across the blocks in range 32608011 to 33039007. The blue plot reflects the revenue discovered on the block state by our algorithm, while the green plot shows the total realized revenue by arbitrages executed in every block.
• Figure 2: Histogram of state update deltas among 430996 blocks built between Thu, 05 Oct 2023 and Sat, 21 Oct 2023.
• Figure 3: Discovered revenue difference (in %) between $\tau$ values and $\tau=\infty$ over time.
• Figure 4: Discovered revenue difference (in %) between profit-maximizing arbitrage selection and FCFS selection strategies, measured for varying $\tau$ values over time.
• Figure 5: Network topology of three scenarios discussed in Section \ref{['sec:network_experiments']} with legend on in Figure a). Dashed blue arrows connecting peers corresponds to delay of 100.