Achieve Better Endorsement Balance on Blockchain Systems

At the beginning of the transaction flow in blockchain system Hyperedger Fabric, client have to select endorsement peer nodes to send proposals. Fabric provides two ways for client: static configuration and random selection using service discovery. However, when a large number of transaction requests arrive, the assignment of tasks to endorsement peer nodes may become unfair. The result of the above situation may lead to the high utilization of computing resources and the working pressure of Fabric nodes, which will eventually affect the transaction performance and throughput. In this paper, we propose a calculation method to accurately measure peer node endorsement load and a dynamic stochastic load minimization algorithm, DSLM. Using DSLM to dynamically determine endorsed peer nodes based on real-time work load will make the distribute of endorsement tasks more balanced. In addition, we implement our approach on Hyperledger Fabric v1.4 LTS and carry out a series of evaluation experiments. By observing the experimental results, the proposed method can effectively balance and reduce the overall workload, and improve the performance of the blockchain system in the case of high concurrency.