A Non-Intrusive Approach to Cross-Environment Server Bottleneck Diagnosis via Packet-Captured Application Latency and APM Metrics

In the process of digital transformation, the performance diagnosis of server systems is crucial for ensuring service continuity and enhancing user experience. Addressing the issues of invasiveness, poor universality, and difficulty in precisely locating abnormal bottlenecks in service requests with traditional performance analysis methods, this paper proposes a nonintrusive diagnosis method named Cross-Environment Server Diagnosis with Fusion (CSDF), which is based on the fusion of network traffic and Application Performance Management (APM) metrics. This CSDF method uses a traffic replay tool to reproduce real service requests captured via network cards in a production environment at a 1:1 ratio in a replay environment, comparing performance differences between the two environments to identify abnormal bottlenecks. By integrating Key Performance Indicator (KPI) metrics collected from APM systems, a correlation model between metrics and bottlenecks is established using the Random Forest algorithm within CSDF to pinpoint the root cause at the host resource layer. Simultaneously, it supplements network layer bottleneck analysis by parsing network transmission characteristics of data packets as an important part of CSDF. Experimental results demonstrate that this CSDF method can effectively identify abnormal bottlenecks in specific service requests, verifying its effectiveness in China Tower’s production system—the correlation coefficient between 1 min average load and latency reached 0.87, and the optimization effect was significant. This study provides a general framework for the precise diagnosis and optimization of server systems via CSDF, possessing strong practical value and promising application prospects.