Stateful to Stateless: Modelling Stateless Ethereum

TL;DR

This paper models the impact of Stateless Ethereum on the network's health using a Bayesian Network, combining empirical data and expert insights to ensure secure and efficient ecosystem operation.

Contribution

It introduces a Bayesian Network model to analyze the effects of Stateless Ethereum, integrating data and expert knowledge for ecosystem health assessment.

Findings

Ethereum ecosystem is expected to remain healthy after Stateless Ethereum implementation.

The Bayesian model captures key factors and their interactions.

Analysis helps identify potential network risks and benefits.

Abstract

The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the introduction of 'witnesses' into the ecosystem. The changes and potential consequences that these additional data packets pose on the network need to be identified and analysed to ensure that the Ethereum ecosystem can continue operating securely and efficiently. In this paper we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum mainnet, the public Ethereum blockchain, focussing on the changes being introduced by Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem. We use a mixture of empirical data and expert knowledge, where data are unavailable, to quantify the model. Based on the data and expert knowledge available to use at the time of modelling, the Ethereum ecosystem is expected to remain healthy following the introduction of Stateless Ethereum.