Meta reinforcement learning based dynamic tuning for blockchain systems in diverse network environments

Abstract

The evolution of blockchain technology across various areas has highlighted the importance of optimizing blockchain systems' performance, especially in fluctuating network bandwidth conditions. We observe that the performance of blockchain systems exhibits variations, and the optimal parameter configuration shifts accordingly when changes in network bandwidth occur. Current methods in blockchain optimization require establishing fixed mappings between various environments and their optimal parameters. However, this process exhibits poor sample efficiency and lacks the ability for fast adaptation to novel bandwidth environments. In this paper, we propose MetaTune, a meta-Reinforcement-Learning (meta-RL)-based dynamic tuning method for blockchain systems. MetaTune can quickly adapt to unknown bandwidth changes and automatically configure optimized parameters. Through empirical evaluations of a real-world blockchain system, ChainMaker, we demonstrate that MetaTune significantly reduces the training samples needed for generalization across different bandwidth environments compared to non-adaptive methods. Our findings suggest that MetaTune offers a promising approach for efficiently optimizing blockchain systems in dynamic network environments.