MH-Raft: an efficient and low-latency consensus algorithm for distributed systems via MOEA/D and hybrid hierarchical clustering

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.

Keywords

Consensus algorithm / Blockchain / Multi-objective evolutionary algorithm / Distributed systems

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The Authors. Published by Zhejiang University Press Co., Ltd.