Transaction Spatio-Temporal Distribution for Permissioned Blockchain Performance Profiling

Blockchain systems, characterized by decentralization, irreversibility, and traceability, have attracted widespread adoption across security-critical domains. However, the discrepancy between claimed and observed performance metrics poses significant challenges for system selection and reliability assurance. Particularly, in multi-layer blockchain architectures with complex inter-node interactions, pinpointing abnormal nodes or malfunctioning stages remains a non-trivial task. While macroscopic metrics, such as transactions per second (TPS) and average latency are essential for measuring system capacity, they lack the granularity to capture fine-grained operational anomalies. To complement these metrics and provide a new diagnostic dimension, we propose a novel metric system grounded in the spatio-temporal transition of transaction states, introducing three complementary indicators: overall spatio-temporal transition cost, spatial transition cost, and temporal transition cost. These metrics characterize system-wide behavior and enable anomaly detection at both the node and stage levels. We further develop a log-driven analysis framework that leverages these metrics for anomaly localization and root cause inference. Our system is implemented atop ChainMaker, deployed over 16 containerized nodes using Docker and Kubernetes. Experimental results demonstrate that our proposed metrics exhibit strong stability, sensitivity, and precision in detecting abnormal behaviors under varied execution conditions. These results validate the effectiveness of our methodology in providing fine-grained insights into the performance reliability of blockchain systems.