The state-of-the-art HotStuff operates an efficient pipeline in which a stable leader drives decisions with linear communication. However, with the unifying proposing-voting pattern, it takes two rounds of messages to produce a certified proposal, which severely limits the performance of the consensus protocol and makes it difficult for the blockchain to exert the bandwidth and concurrency of modern operating systems. Thus, this paper developed a new consensus protocol, called Multi-pipeline HotStuff, for permissioned blockchain. To the best of the authors’ knowledge, this is the first protocol that combines multiple pipelines of HotStuff to propose batches in order, such that proposals are built optimistically when a correct replica realizes that the current proposal is valid and will be certified by quorum votes in the near future. Simultaneous proposing and voting allow the protocol to produce more proposals in every two rounds of messages, it further boosts the throughput at a comparable latency with that of HotStuff. The evaluation experiment confirmed that the throughput of the proposed protocol outperformed HotStuff by approximately 60% without significantly increasing end-to-end latency under varying system sizes. Even if the protocol frequently performs view-change phase due to network asynchrony, its optimization continues to demonstrate better performance.

In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniformed manner. We implemented DLB and deployed it on a practical Cloud environment using CloudSim. Experimental results using both synthetic and real-world data sets show that compared with traditional hash function based load balancing methods, DLB is able to achieve more balanced mappings, especially when the workload is highly skewed.