English

Transaction Fee Market Design for Parallel Execution

Computer Science and Game Theory 2025-08-11 v2 Distributed, Parallel, and Cluster Computing

Abstract

Given the low throughput of blockchains like Bitcoin and Ethereum, scalability - the ability to process an increasing number of transactions - has become a central focus of blockchain research. One promising approach is the parallelization of transaction execution across multiple threads. However, achieving efficient parallelization requires a redesign of the incentive structure within the fee market. Currently, the fee market does not differentiate between transactions that access multiple high-demand storage keys (i.e., unique identifiers for individual data entries) versus a single low-demand one, as long as they require the same computational effort. Addressing this discrepancy is crucial for enabling more effective parallel execution. In this work, we aim to bridge the gap between the current fee market and the need for parallel execution by exploring alternative fee market designs. To this end, we propose a framework consisting of two key components: a Gas Computation Mechanism (GCM), which quantifies the load a transaction places on the network in terms of parallelization and computation, measured in units of gas, and a Transaction Fee Mechanism (TFM), which assigns a price to each unit of gas. We additionally introduce a set of desirable properties for a GCM, propose several candidate mechanisms, and evaluate them against these criteria. Our analysis highlights two strong candidates: the weighted area GCM, which integrates smoothly with existing TFMs such as EIP-1559 and satisfies a broad subset of the outlined properties, and the time-proportional makespan GCM, which assigns gas costs based on the context of the entire block's schedule and, through this dependence on the overall execution outcome, captures the dynamics of parallel execution more accurately.

Keywords

Cite

@article{arxiv.2502.11964,
  title  = {Transaction Fee Market Design for Parallel Execution},
  author = {Bahar Acilan and Andrei Constantinescu and Lioba Heimbach and Roger Wattenhofer},
  journal= {arXiv preprint arXiv:2502.11964},
  year   = {2025}
}

Comments

This is the extended version of the paper, forthcoming at Advances in Financial Technologies (AFT) 2025

R2 v1 2026-06-28T21:47:26.271Z