Developing Cost-Effective Blockchain-Powered Applications

Abstract

Ethereum is a blockchain platform that hosts and executes smart contracts. Executing a function of a smart contract burns a certain amount of gas units (a.k.a., gas usage). The total gas usage depends on how much computing power is necessary to carry out the execution of the function. Ethereum follows a free-market policy for deciding the transaction fee for executing a transaction. More specifically, transaction issuers choose how much they are willing to pay for each unit of gas (a.k.a., gas price). The final transaction fee corresponds to the gas price times the gas usage. Miners process transactions to gain mining rewards, which come directly from these transaction fees. The flexibility and the inherent complexity of the gas system pose challenges to the development of blockchain-powered applications. Developers of blockchain-powered applications need to translate requests received in the frontend of their application into one or more smart contract transactions. Yet, it is unclear how developers should set the gas parameters of these transactions given that (i) miners are free to prioritize transactions whichever way they wish and (ii) the gas usage of a contract transaction is only known after the transaction is processed and included in a new block. In this article, we analyze the gas usage of Ethereum transactions that were processed between Oct. 2017 and Feb. 2019 (the Byzantium era). We discover that (i) most miners prioritize transactions based on their gas price only, (ii) 25% of the functions that received at least 10 transactions have an unstable gas usage (coefficient of variation = 19%), and (iii) a simple prediction model that operates on the recent gas usage of a function achieves an R-Squared of 0.76 and a median absolute percentage error of 3.3%. We conclude that (i) blockchain-powered application developers should be aware that transaction prioritization in Ethereum is frequently done based solely on the gas price of transactions (e.g., a higher transaction fee does not necessarily imply a higher transaction priority) and act accordingly and (ii) blockchain-powered application developers can leverage gas usage prediction models similar to ours to make more informed decisions to set the gas price of their transactions. Lastly, based on our findings, we list and discuss promising avenues for future research.