[Paper Review] Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective

Luyao Zhang, Fan Zhang|arXiv (Cornell University)|May 4, 2023

Blockchain Technology Applications and Security13 citations

TL;DR

The paper empirically analyzes how Ethereum's Merge and EIP-1559 affect transaction waiting time, network load, and market congestion using RDD, time-series forecasting, and network analysis, and proposes block interval design insights.

ABSTRACT

Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, which is essential for computer security and economic efficiency. Integrating data from the Ethereum blockchain and memory pool (mempool), we explore how two types of events affect transaction latency. First, we apply regression discontinuity design (RDD) to study the causal inference of the Merge, the most recent significant upgrade of Ethereum. Our results show that the Merge significantly reduces the long waiting time, network loads, and market congestion. In addition, we verify our results' robustness by inspecting other compounding factors, such as censorship and unobserved delays of transactions via private changes. Second, examining three major protocol changes during the merge, we identify block interval shortening as the most plausible cause for our empirical results. Furthermore, in a mathematical model, we show block interval as a unique mechanism design choice for EIP1559 TFM to achieve better security and efficiency, generally applicable to the market congestion caused by demand surges. Finally, we apply time series analysis to research the interaction of Non-Fungible token (NFT) drops and market congestion using Facebook Prophet, an open-source algorithm for generating time-series models. Our study identified NFT drops as a unique source of market congestion -- holiday effects -- beyond trend and season effects. Finally, we envision three future research directions of TFM.

Motivation & Objective

• Investigate how the Ethereum Merge affects transaction latency under the EIP-1559 TFM.
• Identify unobserved factors and confounders that might influence transaction waiting time.
• Develop a mathematical model to explain the Merge's impact on TFM and guide future design.
• Examine how NFT drops interact with market congestion and demand surges.

Proposed method

• Apply Regression Discontinuity Design (RDD) to estimate local average treatment effects of the Merge on waiting time, network load, and congestion.
• Use Facebook Prophet to decompose and forecast time-series components including trend, seasonality, and holiday effects (NFT drops).
• Utilize Python NetworkX to analyze network structures of transactions, including OFAC-sanctioned activity, before and after the Merge.
• Define waiting time as mempool appearance to blockchain inclusion and compare pre/post-Merge metrics (quantiles and IQR).
• Model block interval changes as a design feature affecting base-fee adjustments under EIP-1559 to explain observed improvements.
• Discuss confounding factors such as private MEV channels and OFAC sanctions and assess robustness.

Experimental results

Research questions

• RQ1 How did the Merge affect latency in the EIP-1559 transaction fee mechanism (ceteris paribus)?
• RQ2 What unobserved factors or confounders might influence transaction waiting time post-Merge?
• RQ3 Can a mathematical model abstract the major Merge impacts to guide future TFM design (infinitum/ad infinitum mutatis mutandis)?
• RQ4 How do NFT drops interact with market congestion and demand surges under the EIP-1559 framework?

Key findings

• The Merge reduces high waiting-time risk, network loads, and market congestion despite a slight increase in transaction arrival rate (from 12.079 to 12.997 per second).
• Waiting time upper quantile (75%) drops by 13.4 seconds (from 35.0 to 21.6 seconds) with p-values < 0.01.
• Intrablock waiting time (IQR) drops by 26.1 seconds (from 54.5 to 28.4 seconds) with p-values < 0.01.
• Network load metrics show a significant reduction (e.g., 28.55%, 28.50%, and 22.31% for 1-, 5-, and 7200-block moving averages) with p-values < 0.01.
• Market congestion risk decreases: odds of congestion drop by 52.72% (and 41.08% for 5 continued blocks) with p-values < 0.01.
• The reduction in waiting time and congestion is most plausibly attributed to the block-interval change in the Merge, enabling faster base-fee adjustments during demand surges.
• NFT drops are identified as holiday-like effects that help forecast and explain persistent congestion patterns using time-series models.