Real-Time Operational Analytics for IT System Health Monitoring
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Solution Overview
Problem
Information technology systems generate vast amounts of log data, making it difficult for administrators to identify the source of failures or anomalies due to dependencies between applications, high log entry volumes, and delayed manifestation of issues, which complicates system health monitoring and maintenance.
Innovation Solution
A system providing real-time operational analytics that ingests log entries, uses a modular framework for scalability, and employs time series predictions to identify errors and anomalies as they occur, including a log collector, parser, feature counter, and analytics engines to process and visualize health data for administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually review log data to identify system issues, then they can detect errors and anomalies, but the volume of log entries makes proper review difficult and time-consuming
Solution Approach 1:
The patent introduces an intermediary system comprising log collectors, parsers, feature counters, and analytics engines that automatically process log data between the log sources and administrators. This intermediary layer aggregates, filters, and analyzes millions of log entries, presenting only relevant anomalies to administrators, thereby maintaining detection accuracy while dramatically reducing review time
Solution Approach 2:
The patent replaces the mechanical manual review process with automated computational systems including log parsers that extract features, counters that track occurrences, and analytics engines that detect anomalies using statistical methods. This substitution eliminates the time constraint of manual review while preserving error detection capability
2Reliability
If administrators review all log entries to ensure comprehensive monitoring, then they can identify all issues, but the vast volume of log data makes comprehensive review impractical
Solution Approach 1:
The patent extracts only the critical and anomalous information from the vast log data using automated analytics engines that identify deviations from normal patterns. Instead of reviewing all log entries, the system extracts and presents only the relevant anomalies to administrators, maintaining monitoring completeness while improving review efficiency
Solution Approach 2:
The patent changes the parameter of log data representation from raw individual entries to aggregated statistical features and anomaly scores. The analytics engines transform millions of log entries into condensed metrics that highlight system health status, enabling comprehensive monitoring without reviewing every single log entry
3Speed
If the system processes and analyzes all log data in real-time to identify issues as they occur, then immediate detection is achieved, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the log analysis system into modular components: log collectors that gather data, parsers that extract features, counters that track occurrences, and analytics engines that detect anomalies. This segmentation allows real-time processing capability while managing system complexity through modular, independently deployable components that can be scaled according to needs
Data Source
AI summary
Application data is received from a plurality of monitored applications. The application data is parsed into a plurality of features describing an operation of the plurality of monitored applications. A counter associated with at least one of the plurality of features is incremented. A system health is derived for the plurality of monitored applications from the counter.