Blockchain Bottleneck Analysis Based on Performance Metrics Causality.

With the widespread application of blockchain technology across various industries, detecting and analyzing performance bottlenecks is crucial for evaluating and optimizing blockchain system performance. However, current research needs general performance metrics for detecting and analyzing bottlenecks. Only some studies focus on this aspect within blockchain systems. To address this, this paper first proposes 18 fine-grained performance metrics to evaluate performance across various layers of blockchain systems comprehensively. Subsequently, we introduce a generalized loosely coupled performance measurement framework to capture these metrics and construct the causal relationship between them, i.e., the mesoscopic performance structure. This approach allows for the detection and analysis of performance bottlenecks. Finally, numerous experimental results demonstrate that the causality between the relevant performance metrics disappears when the system reaches a performance bottleneck. Additionally, the framework has a performance impact of less than 15% on ChainMaker. [ABSTRACT FROM AUTHOR]