Server Downtime Diagnosis System: Architecture, Implementation, and Results
The article explains why a downtime diagnosis system is needed, outlines its architecture and implementation methods—including log sources, feature extraction, and API integration—and presents early results showing high automation coverage and significant operational cost savings.
As business grows, the number of servers and corresponding failures increase, making it essential to diagnose the causes of server downtime to improve stability.
Why a downtime diagnosis system? Manual analysis is time‑consuming, limited in scope, lacks systematic knowledge accumulation, and becomes increasingly difficult as server counts rise.
Alibaba's Server System Innovation Team offers a dedicated downtime diagnosis product that provides API‑based fault analysis and real‑time log monitoring, enabling automatic identification of known issues and proactive risk detection.
Implementation methods
Two prerequisites: logs and log features. Sources include CONMAN (out‑of‑band serial logs via BMC) and SEL (BMC event logs). Features are extracted from massive downtime data, categorized by component, priority, frequency, and time range, covering about 80% of cases.
The diagnostic workflow relies on a feature library; matching is performed via string scans or inverted‑index tokenization as the rule set grows.
Preliminary results
Automated analysis now covers 95% of scenarios, saving millions of dollars annually and reducing manual effort; downtime detection coverage reached 90% within months, allowing quality experts to focus on critical issues.
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