Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background
2.1 Rollup
2.2 EIP-4844
2.3 VAR(Vector Autoregression)


Data
3.1 Consensus security data
3.2 Ethereum usage data
3.3 Rollup Transactions Data
3.4 Blob gas fee data


Empirical Results
4.1 Consensus security
4.2 Ethereum usage
4.3 Rollup transactions
4.4 Blob gas fee market


Conclusion and References Background
2.1 Rollup
2.2 EIP-4844
2.3 VAR(Vector Autoregression) Background 2.1 Rollup 2.1 Rollup 2.2 EIP-4844 2.2 EIP-4844 2.3 VAR(Vector Autoregression) 2.3 VAR(Vector Autoregression) Data
3.1 Consensus security data
3.2 Ethereum usage data
3.3 Rollup Transactions Data
3.4 Blob gas fee data Data 3.1 Consensus security data 3.1 Consensus security data 3.2 Ethereum usage data 3.2 Ethereum usage data 3.3 Rollup Transactions Data 3.3 Rollup Transactions Data 3.4 Blob gas fee data 3.4 Blob gas fee data Empirical Results
4.1 Consensus security
4.2 Ethereum usage
4.3 Rollup transactions
4.4 Blob gas fee market Empirical Results 4.1 Consensus security 4.1 Consensus security 4.2 Ethereum usage 4.2 Ethereum usage 4.3 Rollup transactions 4.3 Rollup transactions 4.4 Blob gas fee market 4.4 Blob gas fee market Conclusion and References Conclusion and References Conclusion and References A. Consensus Security Data A. Consensus Security Data B. Rollup Data Collection B. Rollup Data Collection C. Detailed Var Model Results for Blob Gas Base Fee and Gas Fee C. Detailed Var Model Results for Blob Gas Base Fee and Gas Fee D. Detailed Var Model Results for Blob Gas Base Fee and Blob Gas Priority Fee D. Detailed Var Model Results for Blob Gas Base Fee and Blob Gas Priority Fee E. Rollup Transaction Dynamics E. Rollup Transaction Dynamics 4.4 Blob gas fee market Understanding the blob gas fee market dynamics is essential for enhancing market predictability, enabling DApps and rollups to optimize data posting and fees. This predictability reduces variability in Ethereum transaction settlement times and minimizes costs. Previous research, such as [9, 10], has investigated optimal strategies in multidimensional markets, providing practical insights that complement theoretical models. Additionally, insights from this analysis could contribute to improvements in the blob gas fee market. Extensive studies on gas fee markets [5, 29, 39] have explored how fee update rules might better capture user demands and reduce variability. Extending these studies to multidimensional fee markets could deepen our understanding. This section presents an analysis of the newly emerged blob gas fee market and outlines the following key findings: (1) The VAR model indicates that the gas base fee has a small, yet statistically significant influence on the blob gas base fee. Initially, this impact is positive but tends to diminish over time. (2) We introduced a metric for ‘blob gas priority fee’ to represent the priority demand for blob gas. The validity of this proxy was established by demonstrating its utility in enhancing the explainability of blob gas base fees. (3) The blob gas fee market exhibits higher volatility compared to the gas fee market, indicating potential challenges in predictability and stability. Despite its volatility, the lower ratio of priority fee to base fee in the blob gas market suggests it captures market demands more effectively than the gas market. 4.4.1 Inter relationships between gas and blob gas market. To investigate the dynamic interactions between the gas and blob gas markets, we employed a VAR model analyzing the base fees for both types of gas. The significant effects detected in the model are outlined in Table 4. For the gas base fee, positive effects are noted at lags 1 and 4, with a negative effect at lag 3, indicating oscillating impacts that diminish over time. In contrast, the equation for the blob gas base fee showed no significant effects. The near-zero correlation of residuals (-0.027446) suggests minimal unforeseen shared variations between these markets. 4.4.1 Inter relationships between gas and blob gas market. 4.4.2 Blob gas priority fee. We analyzed the economic implications of blob transactions by examining priority fees—calculated as the difference between the effective gas fee and the base fee. Median priority fees for blob and non-blob transactions were compared across various blocks. As shown in Figure 15, blob transactions have a higher average median priority fee of 1.43 Gwei, which is 45.2% greater than the 0.99 Gwei for non-blob transactions. 4.4.2 Blob gas priority fee. This notable difference implies that blob transactions are typically assigned a higher priority due to the need to handle additional blob data. Consequently, we have defined the priority fee for blobs, termed ‘blob gas priority fee,’ using the following formula, as detailed in Section 3.4: The median gas priority fee from other transactions within the same block serves as the baseline. This baseline is subtracted from the total priority fee to isolate the component attributable to blob gas. This method underscores the additional costs imposed on blob transactions. Validating blob gas priority fee. The priority fee can act as a leading indicator of the base fee when the latter does not promptly reflect demand spikes. Conversely, a rising base fee, signaling increased demand, typically causes the priority fee to decrease. We validated the blob gas priority fee as a proxy for unmet demand using a Vector Autoregression (VAR) analysis, with findings detailed in Table 5 showing statistically significant interactions. Validating blob gas priority fee. Panel A of the table illustrates the persistence of the blob gas priority fee across all time lags, indicating that these fees are applied consistently and reflect strategic adjustments within the network. Notably, a negative coefficient for the blob gas base fee at lag 1 suggests that a higher initial base fee might reduce subsequent priority fees, better aligning with market demands. Panel B demonstrates a positive influence of the blob gas priority fee on the blob gas base fee, confirming that increases in the priority fee are promptly followed by rises in the base fee. These results confirm the interrelationship between the priority and base fees, justifying the use of the blob gas priority fee metric. This metric provides essential insights into fee dynamics within the blob market, aiding developers and users in optimizing network interactions. 4.4.3 Disscussion of Blob Gas Fee Mechanisms. The blob gas base fee update rule’s design is crucial for ensuring predictability for users. Previous studies often evaluate the gas base fee update rule based on two criteria: its volatility and how well it reflects actual demand, inferred from indirect metrics such as the effective gas fee [9, 14, 39]. 4.4.3 Disscussion of Blob Gas Fee Mechanisms. In our analysis, the blob gas priority fee served as a proxy to gauge the deviation from actual demand within the blob gas market. We employed the ratio of the base fee to the priority fee as a critical metric, reflecting the proportion of unmatched user demands. Figure 16 illustrates significant differences between the gas and blob gas markets. The ratio in the gas market stands at 0.037, markedly higher than the blob gas market’s 0.004. This disparity indicates that the blob gas market aligns more closely with user demand, despite its higher volatility, as detailed in Table 6. These observations indicate that the blob gas base fee generally aligns well with user demands, suggesting that the underlying mechanisms are effective. However, its heightened volatility poses challenges for the predictability and stability of transaction costs, which are critical for user strategies and overall market dynamics. This variability requires careful consideration; protocol designers must weigh trade-offs between market responsiveness and fee stability to improve the ecosystem’s operational efficiency 5 CONCLUSION We have conducted a comprehensive analysis of EIP-4844 across four key dimensions: consensus security, Ethereum usage, rollup transactions, and user delays, and the blob gas fee market. Our analysis of the impact of EIP-4844 on the increase in fork rates and block delays provides essential insights into the security implications of the protocol update, addressing concerns within the Ethereum community. 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[47] Leon Visscher, Mohammed Alghazwi, Dimka Karastoyanova, and Fatih Turkmen. 2022. Poster: Privacy-preserving genome analysis using verifiable off-chain computation. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 3475–3477. [48] Toni Wahrstätter. 2024. On Increasing the Block Gas Limit. https://ethresear.ch/ t/on-increasing-the-block-gas-limit/18567. Accessed: 2024-04-15. [49] Gavin Wood et al. 2014. Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper 151, 2014 (2014), 1–32. [50] Yang Xiao, Ning Zhang, Wenjing Lou, and Y Thomas Hou. 2020. Modeling the impact of network connectivity on consensus security of proof-of-work blockchain. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 1648–1657. Authors:
(1) Seongwan Park, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (sucre87@snu.ac.kr);
(2) Bosul Mun, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (bsbs8645@snu.ac.kr);
(3) Seungyun Lee, Seoul National University, Seoul, Repulic of Korea;
(4) Woojin Jeong, Seoul National University, Seoul, Repulic of Korea;
(5) Jaewook Lee, Seoul National University, Seoul, Repulic of Korea;
(6) Hyeonsang Eom, Seoul National University, Seoul, Repulic of Korea;
(7) Huisu Jang (Corresponding author), Soongsil University, Seoul, Republic of Korea. Authors: Authors: (1) Seongwan Park, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (sucre87@snu.ac.kr); (2) Bosul Mun, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (bsbs8645@snu.ac.kr); (3) Seungyun Lee, Seoul National University, Seoul, Repulic of Korea; (4) Woojin Jeong, Seoul National University, Seoul, Repulic of Korea; (5) Jaewook Lee, Seoul National University, Seoul, Repulic of Korea; (6) Hyeonsang Eom, Seoul National University, Seoul, Repulic of Korea; (7) Huisu Jang (Corresponding author), Soongsil University, Seoul, Republic of Korea. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. available on arxiv available on arxiv

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How Blob Gas Priority Fees Influence Ethereum’s Transaction Costs

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