Optimal Sharding for Dynamic Throughput Optimization in Blockchain Systems with Deep Reinforcement Learning.

The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput, which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like Vehicle-to-vehicle (V2V) communication. To address the above issue, the sharding technology, which divides the network into multiple disjoint groups so that transactions can be processed in parallel, is applied to the blockchain systems as a promising solution to improve TPS. This paper considers the Optimal Blockchain Sharding (OBCS) problem which is formulated as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL) based methods where the action space has to be discretized such that it is not too large for tractability. However, the discretization degrades the solution quality since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and propose a sharding control algorithm based on Parametrized Deep Q-Networks (P-DQN) to efficiently handle the discrete-continuous hybrid action space without the scalability issue. Experimental results show that our Parametrized Deep Q-Networks Blockchain Sharding (P-DQNBS) method can effectively improve the TPS by up to 20%.