Dynamic Data-Driven Digital Twins for Blockchain Systems
This work addresses a domain-specific problem for blockchain system developers by proposing an incremental improvement to digital twin methods for managing runtime trade-offs.
The paper tackles the blockchain trilemma trade-off between decentralization, scalability, and security by using a dynamic data-driven digital twin with a feedback loop, reinforcement learning, and simulation to optimize decision-making and reduce computational overhead.
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.