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Taining Cheng

Publications and source records attributed to Taining Cheng.

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High Performance Consensus without Duplication: Multi-pipeline Hotstuff

The state-of-the-art HotStuff operates an efficient pipeline in which a stable leader drives decisions with linear communication and two round-trips of message. However, the unifying proposing-voting pattern is not sufficient to improve the bandwidth and concurrency performance of the modern system. In addition, the delay corresponding to two rounds of message to produce a certified proposal in that scheme is a significant performance bottleneck. Thus, this study developed a new consensus protocol, Multi-pipeline HotStuff, for permissioned blockchain. To the best of the authors' knowledge, this is the first protocol that combines multiple HotStuff instances to propose batches in order without a concurrent proposal, such that proposals are made optimistically when a correct replica realizes that the current proposal is valid and will be certified by quorum votes in the near future. Because simultaneous proposing and voting are allowed by the proposed protocol without transaction duplication, it produced more proposals in every two rounds of messages. In addition, it further boosted the throughput at a comparable latency with that of HotStuff. The evaluation experiment conducted confirmed that the throughput of Multi-pipeline HotStuff outperformed that of the state-of-the-art protocols by approximately 60\% without significantly increasing end-to-end latency under varying system sizes. Moreover, the proposed optimization also performed better when it suffers a bad network condition.

cs.DC

DLB: Deep Learning Based Load Balancing

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 uniform 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.

cs.DC