A load balancing scheme based on deep learning in blockchain network

The blockchain technologies taking advantage of decentralization, anonymity, and trust have begun to exert a significant influence on variety applications. A large number of event data will be generated over a period of time in the blockchain networks. There are many different implementations of load balancing with different approaches and purposes in blockchain network. They will generate a large amount of data and resolutions, providing humans with information and the control of events and objects, even in remote physical environments. However, the demands of the interactions in blockchain networks cause heavy traffic or bottlenecks on particular nodes nevertheless decentralized. Therefore, we proposed an agent that applies a deep reinforced learning method to distribute loads in blockchain network. Also, we show the scalability and reliability of the proposed scheme through mathematical modeling.