A recursive reinforced blockchain performance evaluation and improvement architecture: Maximising diversity to improve scalability

Nowadays, Blockchain technology has received widespread attention because of its ability to effectively solve the trust problem in transactions. However, the throughput of crypto currency using Blockchain technology has never been comparable to that of centralized payment institutions. The fundamental reason is the architecture of the Blockchain itself, and the existing methods often focus on the part of the Blockchain but lack the overall control. In this paper, we design, a novel Recursively Reinforced Blockchain Architecture Search (BORAS) architecture, from the perspective of the overall structure of the Blockchain, which can search the various attributes of the various levels in a adaptive manner, aiming at selecting the best configuration in a dynamical environment. First, we introduce a recursive and scalable reinforcement learning framework, to find the optimal block size and block interval of the Blockchain, so as to improve the throughput of the Blockchain system. Second, we add the selection scheme of consensus algorithm to the dynamic space and also use the framework to select the optimal consensus mechanism to realize the optimal configuration of the Blockchain system in the dynamic environment. Third, we adopt a recursive method, which greatly reduces the time complexity required for the optimal configuration of the Blockchain system and improves the configuration efficiency. Comprehensive experiments on three representative Blockchain platforms demonstrate that BOBAS can consistently improve the throughput, while also reducing latency, with remarkable and efficient performances.