Analysis of MEV Ecosystem on Major Public Chains: Interplay Between Architecture and Transaction Ordering
By analyzing the nature of MEV attacks, we examine the current state of the DeFi ecosystem on major public blockchains, as this directly influences the activity of MEV on-chain. We focus on four major and active public chains—Ethereum, Solana, Aptos, and Sui—to explore the relationship between MEV architecture and the evolution of MEV. Our findings show that the MEV system is closely linked to both the blockchain architecture and the transaction ordering mechanisms, which in turn directly affect user experience.
On blockchains such as Sui and Ethereum, which use Gas Fees for transaction ordering, periods of high on-chain activity (such as meme coin trading, significant price movements, or NFT mints) can lead to a vicious cycle of increasing Gas Fees. This creates a barrier for users who cannot participate during these high-traffic times. In Sui, the lack of an EIP-1559-like smoothing mechanism makes Gas Fee fluctuations more extreme, whereas Ethereum has a smoother growth curve due to the introduction of this protocol.
In contrast, the deterministic function ordering model used by Aptos tends to concentrate MEV at the tail end of transactions, as the Leader node only gains the full block view after the ordering process is complete. This results in more complexity in Aptos’ MEV landscape, with fewer opportunities for front-running attacks, and consequently, more stable Gas Fees.
Solana, like Aptos, uses a FCFS (First-Come, First-Served) deterministic model for transaction ordering, which prioritizes speed for Searchers. This results in miners with better hardware earning higher profits. However, the heavy reliance on speed leads to a flood of duplicate transactions sent by bots, trying to maximize their chances of being included in blocks, potentially overwhelming the network. To address this issue, Jito Labs introduced a pseudo-Mempool mechanism similar to Ethereum’s, supporting priority fees to front-run transactions. While this provides some relief, it also brings challenges such as high Gas Fee volatility and an influx of spam transactions.
From the analysis of various public chain architectures and transaction ordering models, we observe that they naturally give rise to different MEV market dynamics, which can be predicted based on transaction behavior and system architecture. For Ethereum, the introduction of EIP-1559 provides a useful response to the Gas Fee prioritization mechanism, redistributing value and smoothing out Gas Fee fluctuations. However, EIP-1559 still does not fully address problems like Sandwich attacks, nor does it eliminate the poor user experience caused by high Gas Fees.
Therefore, current MEV solutions primarily focus on creating a transparent and open market, but targeted solutions to issues like Sandwich attacks are still needed. Additionally, the problems arising from different architectures and derivative protocols require careful consideration. Ethereum and Solana represent two distinct architectures, each facing unique challenges regarding MEV, and thus should be addressed on a case-by-case basis.