Optimizing Transaction Fee for Different Decentralized Applications in Ethereum

Abstract

Typically, users can pay higher transaction fee to reduce confirmation time while submitting a transaction in Ethereum. However, the rising transaction fee becomes a major issue. Therefore, a fine-grained gas price suggestion tool is expected to various decentralized applications (dApps) with different Quality of Services (QoS) requirements. In this paper, we proposed a machine learning based gas price prediction approach for optimizing transaction fee in Ethereum. The proposed approach takes features of dApps into consideration. The experimental results show that the proposed approach can provide fine-grained transaction fee estimation for dApps with various QoS requirements.