Dynamic Data-Driven Digital Twins for Blockchain Systems

TL;DR

This paper explores how dynamic data-driven digital twins, combined with reinforcement learning and simulation, can optimize blockchain systems by balancing decentralization, scalability, and security during runtime.

Contribution

It introduces an approach integrating DDDAS feedback loops with reinforcement learning and simulation to enhance blockchain system management and address the trilemma trade-off.

Findings

Reinforcement learning improves blockchain optimization.

Simulation reduces computational overhead.

Digital twins enable dynamic system management.

Abstract

In recent years, we have seen an increase in the adoption of blockchain-based systems in non-financial applications, looking to benefit from what the technology has to offer. Although many fields have managed to include blockchain in their core functionalities, the adoption of blockchain, in general, is constrained by the so-called trilemma trade-off between decentralization, scalability, and security. In our previous work, we have shown that using a digital twin for dynamically managing blockchain systems during runtime can be effective in managing the trilemma trade-off. Our Digital Twin leverages DDDAS feedback loop, which is responsible for getting the data from the system to the digital twin, conducting optimisation, and updating the physical system. This paper examines how leveraging DDDAS feedback loop can support the optimisation component of the trilemma benefiting from Reinforcement Learning agents and a simulation component to augment the quality of the learned model while reducing the computational overhead required for decision-making.