Research on dynamic Dual-Master Node Consensus Algorithm based on reputation evaluation

Abstract

ObjectiveByzantine Fault-Tolerant (BFT) consensus algorithms in blockchain systems were confronted with two fundamental challenges that significantly impacted their practical implementation: inefficient view changes resulting from dishonest primary nodes and excessive communication overhead during consensus processes.MethodsA novel Dynamic Dual-Master Practical Byzantine Fault Tolerance (DM-PBFT) algorithm was developed to simultaneously overcome these limitations through three key technical innovations. The DM-PBFT architecture was constructed with two tightly integrated components: an optimized consensus process and an intelligent view-change mechanism. To address the critical issue of communication overhead, the algorithm incorporated a dual-primary node framework (designated as B1 and B2) combined with Verifiable Secret Sharing (VSS) to streamline the consensus workflow. In this carefully designed process: (1) client A initiated transactions by sending requests to primary node B1; (2) B1 subsequently broadcast these requests along with A's unique identifier to the network; (3) the secondary primary node B2 distributed cryptographic subkeys and corresponding commitment values to all consensus nodes in set B; (4) each node independently verified the received subkeys using the commitment values to ensure cryptographic integrity; (5) upon successful verification, nodes computed request approval digests incorporating their subkeys and transmitted these digests back to B1; (6) after collecting sufficient subkeys, B1 performed efficient key recovery and compared the results with B2's master key; (7) finally, verification values were broadcast network-wide and persistently stored in all nodes' state databases to complete the consensus process.For optimizing view-change efficiency, a sophisticated reputation evaluation system was implemented based on an enhanced PeerTrust model. This system incorporated multidimensional confidence factors that dynamically synthesized both local interaction history and global reputation metrics through comprehensive analysis of inter-node verification relationships. The algorithm strategically organized nodes into three distinct operational tiers (primary layer, consensus layer, and non-consensus layer), each with specialized reputation evaluation methodologies. View transitions were executed through continuous hierarchical scoring during consensus rounds, with the system automatically adjusting node classifications based on their evolving reputation scores.The view-change protocol incorporated three carefully designed failure recovery procedures: (1) When primary node B1 was identified as Byzantine, the top-ranked consensus node was automatically promoted to replace it while maintaining operational continuity through B2's consistent subkey management; (2) For failures of B2, a similar replacement protocol was activated with additional safeguards to ensure complete subkey redistribution; (3) In the rare case of simultaneous failure of both primary nodes, the two highest-ranked consensus nodes were promoted to form a new primary pair, with the system automatically reinitializing the consensus process. Within the consensus layer, Byzantine nodes were systematically identified through continuous monitoring and temporarily marked rather than immediately replaced. Replacement was only triggered when the concentration of marked Byzantine nodes reached precisely one-third of the consensus layer's capacity, at which point a corresponding number of top-performing nodes from the non-consensus layer were promoted. This threshold-based approach strategically minimized unnecessary view changes while maintaining rigorous fault tolerance guarantees.Through rigorous algorithmic analysis, DM-PBFT was formally proven to satisfy all critical BFT properties: (1) Request messages and subkeys were cryptographically secured through the combined use of advanced signature algorithms and commitment schemes; (2) Consistency was guaranteed through mathematical proof showing all honest nodes would agree on the same sequence of requests; (3) Liveness was ensured by demonstrating the system would always progress within bounded time, even during view changes; (4) Termination was mathematically verified through analysis of the reputation-based view-change protocol. Communication complexity analysis established that DM-PBFT achieved O(n) complexity, representing a significant improvement over PBFT's O(n²) scaling. Time complexity analysis, conducted under realistic asynchronous network models, confirmed the algorithm's superior temporal efficiency compared to existing approaches.ResultsAn extensive experimental evaluation was conducted to validate DM-PBFT's performance across multiple critical dimensions. The testing framework systematically compared DM-PBFT against three established benchmarks (PBFT, reputation grouping, and HotStuff) while examining consensus latency, communication overhead, throughput, and resilience to network dynamics under various operational conditions.In controlled latency testing with network sizes scaling to 500 nodes, DM-PBFT demonstrated remarkable performance, achieving consensus latency of just 0.46176 seconds. This represented a 400× improvement over conventional PBFT (186.44476s), a 100× improvement over reputation grouping (46.09653s), and a 56× improvement over HotStuff (25.8702s). Detailed analysis revealed that while all tested algorithms exhibited increased latency with network growth, DM-PBFT's hierarchical architecture maintained superior scalability, with latency increasing at a sub-linear rate compared to the polynomial growth observed in other approaches.Communication overhead measurements showed that DM-PBFT achieved stable performance after reaching network stratification thresholds, consistently maintaining O(n√n) complexity. In practical terms, this translated to a 62.4% reduction in bandwidth consumption compared to traditional PBFT implementations and measurable improvements over HotStuff's message efficiency. The communication overhead grew gradually with network size until reaching stratification points, after which it remained effectively constant regardless of additional node joins.Throughput benchmarking produced equally impressive results, with DM-PBFT sustaining 3,500 transactions per second (TPS) in large-scale configurations compared to PBFT's 1,200 TPS. While matching HotStuff's peak throughput, DM-PBFT demonstrated significantly better scalability, with throughput improvement rates exceeding those of the reputation grouping approach by substantial margins after crossing stratification thresholds.Additional experiments examined DM-PBFT's resilience under challenging network conditions. Node churn tests confirmed that the frequency of node joins/leaves only impacted performance through net changes in total node count, not through the rate of such changes. Network bandwidth fluctuation tests demonstrated the reputation system's effectiveness in automatically compensating for variable connection quality, with the algorithm maintaining stable performance across diverse bandwidth conditions after several consensus rounds of adaptation.The comprehensive experimental results collectively established DM-PBFT's advantages across three fundamental metrics: (1) Exceptional latency characteristics, delivering 400× improvements at scale; (2) Efficient bandwidth utilization, achieving 62.4% reductions compared to conventional approaches; (3) Consistently high throughput maintenance even under adversarial conditions with Byzantine node concentrations up to 30%. The view-change process demonstrated particular efficiency, completing in just 1.2 seconds compared to PBFT's 12.8 seconds - a 90.6% reduction that proved critical for practical deployment scenarios.ConclusionsThe DM-PBFT algorithm represented a significant theoretical and practical advancement in Byzantine fault-tolerant consensus mechanisms through its novel integration of dual-primary node management, verifiable secret sharing, and dynamic reputation-based stratification. The solution demonstrated particular effectiveness for large-scale consortium blockchain implementations, successfully achieving the dual objectives of sub-linear communication growth and rapid fault recovery without compromising security guarantees.The algorithm's architectural innovations, especially its stratified node management framework and intelligent threshold-based view-change protocol, established a new foundation for next-generation consensus protocol design in increasingly complex and adversarial network environments. Future research directions were identified to further enhance the algorithm's practical utility, including: (1) Optimization for real-world deployment scenarios with heterogeneous hardware; (2) Development of cross-shard coordination mechanisms for sharded blockchain architectures; (3) Enhanced security analysis under sophisticated adaptive adversary models; (4) Integration with emerging cryptographic techniques such as zero-knowledge proofs for additional privacy preservation. These advancements promised to extend DM-PBFT's applicability to an even broader range of production blockchain environments while maintaining its fundamental advantages in efficiency, security, and scalability.