【摘要】 Raft是一种用于分布式系统的基础共识协议，旨在确保机器集群间的状态机复制与数据一致性。然而，传统Raft面临显著的性能瓶颈，尤其是在大规模部署中，选举效率欠佳且共识延迟过高。为应对这些挑战，提出一种名为MH-Raft的增强型共识变体，旨在实现高效率和低延迟。提出一种分层节点管理和选举框架，以优化网络协调。具体而言，制定一种基于分解的多目标进化算法（MOEA/D）的领导者选举方法；该方法通过评估多维节点属性，以最小化选举延迟。为进一步完善所提出的分层架构，针对最优中介节点选择，设计了一个严格的紧密度定义，并将其集成到一个混合聚类算法中，该算法能够自适应划分网络并优化中介节点与跟随者节点之间的映射。综合实验的定量评估表明，与传统Raft实现相比，MH-Raft显著降低整体选举延迟，并将共识延迟降低了14.87%–34.45%，同时将平均吞吐量提高了30.43%。更多还原

【Abstract】 Raft is a foundational consensus protocol for distributed systems, architected to ensure state machine replication and data consistency across machine clusters. However, traditional Raft faces significant performance bottlenecks, particularly regarding suboptimal election efficiency and substantial consensus latency in large-scale deployments. To address these challenges, this study presents MH-Raft, an enhanced consensus variant designed for high efficiency and minimal latency.We propose a hierarchical node management and election framework to optimize network coordination. Specifically, a leader election methodology leveraging the multi-objective evolutionary algorithm based on decomposition(MOEA/D) is formulated to minimize election latency by evaluating multi-dimensional node attributes. To further refine the proposed hierarchical architecture, a rigorous tightness definition is devised for optimal mediator node selection, which is integrated into a hybrid clustering algorithm that adaptively partitions the network and optimizes the mapping between mediator nodes and follower nodes. Quantitative evaluations via comprehensive experiments demonstrate that MH-Raft significantly reduces overall election latency and lowers consensus latency by 14.87%–34.45%, while enhancing average throughput by 30.43% compared to the conventional Raft implementation.更多还原