Transaction fees optimization in the Ethereum blockchain

Laurent, A.; Brotcorne, L.; Fortz, Bernard

doi:10.1016/j.bcra.2022.100074

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Article (Scientific journals)

Transaction fees optimization in the Ethereum blockchain

Laurent, A.; Brotcorne, L.; Fortz, Bernard

2022 • In Blockchain: Research and Applications, 3 (3)

Peer reviewed

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https://hdl.handle.net/2268/308573

DOI
10.1016/j.bcra.2022.100074

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Keywords :

Pricing; Blockchain; Ethereum; Optimization; Gasprice; Ether; Monte-Carlo

Abstract :

[en] In blockchains, transaction fees are fixed by the users. The probability for a transaction to be processed quickly increases with the fee level. In this paper, we study the transaction fee optimization problem in the Ethereum blockchain. This problem consists of determining the minimum price a user should pay so that its transaction is processed with a given probability in a given amount of time. To reach this goal, we define a new solution method based on a Monte Carlo approach to predict the probability that a transaction will be mined within a given time limit. Numerical results on real data highlight the quality of the results.

Disciplines :

Quantitative methods in economics & management
Computer science

Author, co-author :

Laurent, A.

Brotcorne, L.

Fortz, Bernard ; Université de Liège - ULiège > HEC Liège Research > HEC Liège Research: Business Analytics & Supply Chain Mgmt

Language :

English

Title :

Transaction fees optimization in the Ethereum blockchain

Publication date :

2022

Journal title :

Blockchain: Research and Applications

Volume :

Issue :

Peer reviewed :

Peer reviewed

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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136051644&doi=10.1016%2fj.bcra.2022.100074&partnerID=40&md5=c422a3e11ffcd1274260d2c38c08f055

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Bibliography

Buterin, V., Ethereum White Paper. 2014, GitHub Available online: https://github.com/ethereum/wiki/wiki/White-Paper. (Accessed 24 April 2021)
Xu, X., Weber, I., Staples, M., et al. A taxonomy of blockchain-based systems for architecture design. 2017 IEEE International Conference on Software Architecture (ICSA); 3–7 Apr 2017; Gothenburg, Sweden, 2017, IEEE, Piscataway, NJ, USA, 243–252.
Houy, N., The bitcoin mining game. 2014, SSRN preprint.
Szabo, N., Formalizing and securing relationships on public networks. First Monday, 2(9), 1997, 10.5210/fm.v2i9.548.
Wood, G., Ethereum: a secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper, 2014 Available online: https://ethereum.github.io/yellowpaper/paper.pdf. (Accessed 24 July 2020)
Antonopoulos, A.M., Wood, G., Mastering Ethereum: Building Smart Contracts and Dapps. 2018, O'Reilly Media, Sebastopol, CA, USA.
Miller, A., Gas economics. https://github.com/LeastAuthority/ethereum-analyses/blob/master/GasEcon.md, 2015. (Accessed 1 April 2019)
Grech, N., Kong, M., Jurisevic, A., et al. Madmax: surviving out-of-gas conditions in ethereum smart contracts. In: Proceedings of the ACM on Programming Languages, 2018, ACM, New York, NY, USA, 1–27 OOPSLA.
Albert, E., Gordillo, P., Rubio, A., Sergey, I., GASTAP: a gas analyzer for smart contracts. 2018, arXiv.
Marescotti, M., Blicha, M., Hyvärinen, A.E., Asadi, S., Sharygina, N., Computing exact worst-case gas consumption for smart contracts. Margaria, T., Steffen, B., (eds.) Leveraging Applications of Formal Methods, Verification and Validation, 2018, Springer, Cham, Switzerland, 450–465.
Atzei, N., Bartoletti, M., Cimoli, T., A survey of attacks on ethereum smart contracts (sok). Maffei, M., Ryan, M., (eds.) Principles of Security and Trust, 2017, Springer, Heidelberg, Berlin, Germany, 164–186.
Chen, T., Li, X., Luo, X., Zhang, X., Under-optimized smart contracts devour your money. 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER); 20–24 Feb 2017; Klagenfurt, Austria, 2017, IEEE, Piscataway, NJ, USA, 442–446.
Chen, T., Li, X., Wang, Y., et al. An adaptive gas cost mechanism for ethereum to defend against under-priced dos attacks. Liu, J., Samarati, P., (eds.) Information Security Practice and Experience. ISPEC 2017, 2017, Springer, Cham, Switzerland, 3–24.
Weber, I., Gramoli, V., Ponomarev, A., et al. On availability for blockchain-based systems. 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS); 26–29 Sept 2017; Hong Kong, China, 2017, IEEE, Piscataway, NJ, USA, 64–73.
Pierro, G.A., Rocha, H., The influence factors on ethereum transaction fees. 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB); 27 May 2019; Montreal, QC, Canada, 2019, IEEE, Piscataway, NJ, USA, 24–31.
Sousa, J.E.d.A., Oliveira, V., Valadares, J., et al. An analysis of the fees and pending time correlation in ethereum. Int. J. Netw. Manag., 31(3), 2021, e2113.
Karaivanov, A., Donmez, A., Transaction Fee Economics in the Ethereum Blockchain., 2021, IDEAS Available online: https://ideas.repec.org/p/sfu/sfudps/dp21-02.html. (Accessed 24 July 2020)
Easley, D., O'Hara, M., Basu, S., From mining to markets: the evolution of bitcoin transaction fees. J. Financ. Econ. 134:1 (2019), 91–109.
Ricci, S., Ferreira, E., Menasché, D.S., Ziviani, A., Souza, J.E., Vieira, A.B., Learning blockchain delays: a queueing theory approach. Perform. Eval. Rev. 46:3 (2019), 122–125.
Koops, D., Predicting the Confirmation Time of Bitcoin Transactions. 2018 arXiv preprint arXiv:1809.10596.
Pierro, G.A., Rocha, H., Ducasse, S., Marchesi, M., Tonelli, R., A user-oriented model for oracles' gas price prediction. Future Generat. Comput. Syst. 128 (2022), 142–157.
Pierro, G.A., Rocha, H., Tonelli, R., et al. Are the gas prices oracle reliable? a case study using the ethgasstation. 2020 IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE); 18 Feb 2020; London, ON, Canada, 2020, IEEE, Piscataway, NJ, USA, 1–8.
Singh, H.J., Hafid, A.S., Transaction confirmation time prediction in ethereum blockchain using machine learning. 2019, arXiv.
Liu, F., Wang, X., Li, Z., Xu, J., Gao, Y., Effective gasprice prediction for carrying out economical ethereum transaction. 2019 6th International Conference on Dependable Systems and Their Applications (DSA); 3–6 Jan 2020; Harbin, China, 2020, IEEE, Piscataway, NJ, USA, 329–334.
Werner, S.M., Pritz, P.J., Perez, D., Step on the gas? A better approach for recommending the ethereum gas price. Pardalos, P., Kotsireas, I., (eds.) Mathematical Research for Blockchain Economy, 2020, Springer, Cham, Switzerland, 161–177.
Carl, D., Ewerhart, C., Ethereum gas price statistics. 2020, SSRN University of Zurich, Working Paper No. 373.
Mars, R., Abid, A., Cheikhrouhou, S., Kallel, S., A machine learning approach for gas price prediction in ethereum blockchain. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC); 12–16 Jul 2021; Madrid, Spain, 2021, IEEE, Piscataway, NJ, USA, 156–165.
Chuang, C., Lee, T., A practical and economical bayesian approach to gas price prediction. Awan, I., Benbernou, S., Younas, M., (eds.) The International Conference on Deep Learning, Big Data and Blockchain, 2021, Springer, Cham, Switzerland, 160–174.
Lien, C.-C., Su, W.-T., A qos-driven customizable forecasting framework for blockchain transaction fee recommendation. 2021 IEEE International Conference on Smart Internet of Things (SmartIoT); 13–15 Aug 2021; Jeju, Republic of Korea, 2021, IEEE, Piscataway, NJ, USA, 348–349.