The Blockchain Execution Dilemma: Optimizing Revenue XOR Fair Ordering

2026-04-25

AI Summary

This study addresses a fundamental trade-off in blockchain execution layers between validator revenue and transaction fairness, which traditional sequencing approaches struggle to balance. The authors propose a chain-agnostic, continuous transaction ordering model that, for the first time, enables joint optimization of revenue and fairness in environments supporting overlapping block execution. Their dynamic optimization framework integrates gas prices, transaction destinations, and predicted execution times, while innovatively employing an interruptible genetic algorithm for sequence scheduling at arbitrary time points. Evaluated on real-world data from Sui and Ethereum, the method increases validator profits by approximately 15% and accelerates congestion alleviation by up to 58%. Quantitative analysis further reveals that enforcing strict fairness during high-congestion periods can reduce validator revenue by 50%–60%.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The successive generations of consensus algorithms have progressively shifted the performance bottleneck of blockchains to the execution layer. While recent works address this by parallelizing transaction execution, they often overlook the critical role of transaction sequencing. Historically, transaction ordering was left to validator discretion, a practice prone to Maximal Extractable Value (MEV) attacks, or rigid fair-ordering protocols that limit validator revenue. In this work, we address the tension between validator revenue and order fairness using a dynamic optimization framework. We introduce a blockchain-independent model for transaction sequencing in a continuous setting where block executions can overlap. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that our algorithm increases validator profit by approximately 15% and accelerates congestion relief by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing they can reduce validator revenue by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict fair ordering effectively nullifies the advantages of advanced sequencing.

Problem

• transaction ordering
• Maximal Extractable Value
• fair ordering
• validator revenue
• blockchain execution

Innovation

• transaction sequencing
• blockchain execution
• fair ordering
• Maximal Extractable Value (MEV)
• anytime genetic algorithm