Are the Gas Prices Oracle Reliable? A Case Study using the EthGasStation

Pierro, G. A.; Rocha, Henrique; Tonelli, Roberto; Ducasse, Sté́phane

doi:10.1109/iwbose50093.2020.9050186

Cited by 18 publications

(7 citation statements)

References 7 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the authors conclude that transaction features, including gas and gas prices defined by users, cannot determine the pending time of transactions on the Ethereum platform. A plethora of other studies analyze, evaluate or forecast gas prices on Ethereum, i.e., how fees should ideally be set for the initiation of transactions, and find multiple ways for optimization or identify inefficiencies (Feng et al 2023;Bouraga 2020;Antonio Pierro et al 2020;Pierro et al 2022;Mars et al 2021;Chunmiao Li et al 2020;Liu et al 2020;Laurent et al 2022;Werner et al 2020).…”

Section: Ethereum Transaction Feesmentioning

confidence: 99%

Time-Varying Bidirectional Causal Relationships between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

Ante,

Saggu

2024

JRFM

View full text Add to dashboard Cite

The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite processing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally influence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence. These results inform strategic considerations for stakeholders to more effectively plan, utilize, and advocate for economic activities on Ethereum, enhancing the understanding and optimization of within the rapidly evolving economy.

show abstract

Section: Ethereum Transaction Feesmentioning

confidence: 99%

Time-Varying Bidirectional Causal Relationships between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

Ante,

Saggu

2024

JRFM

View full text Add to dashboard Cite

show abstract

“…In addition, there are some other studies on gas, including estimation of gas consumption [53], [122], [123], [124], [125], [126], [127], gas price [128], [129], [130], [131], [132], [133], gas mechanism [134], [135], [136], the out of gas exceptions [137], [138] and others [139]. However, none of the above work uses inline assembly for gas optimization or other purposes.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Empirical Study of Inline Assembly on 7.6 Million Ethereum Smart Contracts

Liao

Song

Zhu

et al. 2023

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Being the most popular programming language for developing Ethereum smart contracts, Solidity allows using inline assembly to gain fine-grained control. Although many empirical studies on smart contracts have been conducted, to the best of our knowledge, none has examined inline assembly in smart contracts. To fill the gap, in this paper, we conduct the first large-scale empirical study of inline assembly on more than 7.6 million open-source Ethereum smart contracts from three aspects, namely, source code, bytecode, and transactions after designing new approaches to tackle several technical challenges. Through a thorough quantitative and qualitative analysis of the collected data, we obtain many new observations and insights. Moreover, by conducting a questionnaire survey on using inline assembly in smart contracts, we draw new insights from the valuable feedback. This work sheds light on the development of smart contracts as well as the evolution of Solidity and its compilers.

show abstract

“…The role of gas price recommenders or oracles in the prediction of the gas price has been discussed by a number of authors [30][31][32][33][34]. In brief, the gas price oracle attempts to predict the future gas price on the basis of previous block utilization.…”

Section: The Role and Performance Of Gas Price Oraclesmentioning

confidence: 99%

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Butler

Crane

2023

Mathematics

View full text Add to dashboard Cite

Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.

show abstract

Are the Gas Prices Oracle Reliable? A Case Study using the EthGasStation

Cited by 18 publications

References 7 publications

Time-Varying Bidirectional Causal Relationships between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

Time-Varying Bidirectional Causal Relationships between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

Large-Scale Empirical Study of Inline Assembly on 7.6 Million Ethereum Smart Contracts

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Contact Info

Product

Resources

About