Trace-Based Dynamic Gas Estimation of Loops in Smart Contracts

Abstract

Smart contracts on Ethereum can be used to encode business logic and have been applied to many different areas, such as token exchanges and games. Unlike general programs, the computations of contracts on Ethereum are restricted by the gas limit. If a transaction runs out of the gas limit before an execution finishes, the Ethereum virtual machine throws an out-of-gas exception, and the entire transaction fails, which reverts to the state before the transaction started, although the transaction fee is still deducted. It is therefore, essential to conduct a gas estimation before sending a transaction. Existing studies have mostly failed in estimating the gas for a loop function because the number of iterations of the loops cannot be statically determined. However, we found that a quarter of all contracts have loop functions, and the gas cost for the loops is higher than for the other functions. Therefore, it is necessary to apply a gas estimation for the loop functions. In this study, we propose a gas estimation approach based on the transaction trace to dynamically estimate the gas for the loop functions. Our belief is that we can learn the relationship between the historical transaction traces and their gas costs to estimate the gas for new transactions. We considered three different abstractions of the original transaction trace and fed them to different machine learning models. The results show that our approach is effective in gas estimation and that a random forest can achieve the most accurate estimation.

Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules

“…For example, executing smart contracts in Ethereum involves gas consumption as the real cost of the transaction. Research works are underway to estimate the gas consumption of loops [46].…”

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

“…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).…”