The Blockchain Execution Dilemma: Optimizing Revenue XOR Fair Ordering

Abstract

Successive consensus algorithm generations displaced the blockchain performance bottleneck to the execution layer. Recent works address this by parallelizing non-conflicting transactions. Historically, execution followed consensus-level ordering, a practice susceptible to Maximal Extractable Value (MEV) exploitation. Conversely, recent academic proposals introduce rigid fair-ordering protocols that severely restrict transaction reordering. Parallel execution frameworks optimize the sequencing layer, located between consensus and execution, assembling transaction batches from committed blocks and transmitting them to workers to maximize execution parallelism and realized fees. To achieve this, current sequencing implementations may defer transactions, but they do not maintain order fairness. Preserving these properties currently requires sequential execution, drastically reducing profitability and performance. In this work, we address the tension between validator profit and order fairness using a dynamic optimization framework. We introduce a blockchain-agnostic model for transaction sequencing in a continuous setting where block sequencing and execution run concurrently. When sequencing cannot complete within the available time window, our framework dynamically returns its best intermediate result. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules, with an option to maintain fair ordering. Evaluating our approach on real-world Sui and Ethereum datasets shows it increases validator profit by ~15% and accelerates congestion relief speed by up to 58%. Furthermore, fair-ordering constraints can reduce validator profit by 50% to 60% during high congestion, providing the first evidence that enforcing strict fair ordering might effectively nullify the advantages of advanced sequencing.