Dynamic Digital Twins of Blockchain Systems: State Extraction and Mirroring

TL;DR

This paper proposes algorithms for creating digital twins of blockchain systems to dynamically extract and mirror their state, aiming to improve scalability and efficiency by adapting consensus protocols to system conditions.

Contribution

It introduces algorithms for extracting and mirroring blockchain states in digital twins, addressing decentralization and synchronization challenges to enhance blockchain management.

Findings

Algorithms are robustly evaluated experimentally.

The approach enables dynamic adaptation of consensus protocols.

Improves blockchain scalability and efficiency.

Abstract

Blockchain adoption is reaching an all-time high, with a plethora of blockchain architectures being developed to cover the needs of applications eager to integrate blockchain into their operations. However, blockchain systems suffer from the trilemma trade-off problem, which limits their ability to scale without sacrificing essential metrics such as decentralisation and security. The balance of the trilemma trade-off is primarily dictated by the consensus protocol used. Since consensus protocols are designed to function well under specific system conditions, and consequently, due to the blockchain's complex and dynamic nature, systems operating under a single consensus protocol are bound to face periods of inefficiency. The work presented in this paper constitutes part of an effort to design a Digital Twin-based blockchain management framework to balance the trilemma trade-off problem, which aims to adapt the consensus process to fit the conditions of the underlying system. Specifically, this work addresses the problems of extracting the blockchain system and mirroring it in its digital twin by proposing algorithms that overcome the challenges posed by blockchains' decentralised and asynchronous nature and the fundamental problems of global state and synchronisation in such systems. The robustness of the proposed algorithms is experimentally evaluated.