Log Analytics Incident Prediction System
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Solution Overview
Problem
Large organizations face challenges in timely troubleshooting and diagnosis of software system failures due to the vast volume of logs generated, which can lead to extended delays and adverse user experiences.
Innovation Solution
A system and method for predicting incidents like system outages using active log monitoring and analysis, involving ingestion of logs into a Hadoop file system, parsing for unique patterns, topic modeling, and machine learning to determine causality and predict incidents in real-time or near real-time, with alerts sent to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reactive analysis of logs by IT staff members is used to diagnose failures, then human expertise can identify the cause of failures, but it takes several hours to find relevant logs and investigate the issue, leading to extended delays
Solution Approach 1:
The system performs preliminary analysis of log data using machine learning models to identify potential incidents before they manifest as actual failures. The incident prediction engine continuously monitors log patterns and predicts probable incidents, allowing the system to alert operators in advance rather than waiting for failures to occur and then manually investigating.
Solution Approach 2:
The patent replaces the manual mechanical process of IT staff searching and analyzing logs with an automated electronic system. The incident prediction engine uses machine learning algorithms to automatically ingest, parse, and analyze log data, substituting human manual investigation with automated computational analysis that operates continuously without fatigue or delay.
2Loss of information
If large volume of logs is generated to capture all system activities, then comprehensive monitoring data is available, but it becomes challenging to troubleshoot software issues due to the vast amount of data
Solution Approach 1:
The system extracts only the relevant information from the vast volume of log data. The incident prediction engine uses machine learning models trained on historical log data to identify and extract meaningful patterns and anomalies, filtering out irrelevant information. This extraction process converts the overwhelming volume of raw logs into concentrated, actionable insights about potential incidents.
Solution Approach 2:
The patent transforms the parameter space of log analysis by changing from analyzing individual log entries to analyzing aggregated patterns and trends. The machine learning models transform raw log parameters into derived features such as frequency of error patterns, temporal trends, and contextual relationships, making the analysis manageable despite the large volume of underlying data.
3Reliability
If manual log analysis is performed to ensure accurate incident diagnosis, then thorough investigation is possible, but it results in extended delays in fixing software applications
Solution Approach 1:
The system replaces manual log analysis with automated machine learning-based analysis. The incident prediction engine continuously processes log data using trained models that have learned from historical incident patterns, providing reliable predictions without human intervention. This substitution maintains diagnostic reliability while dramatically improving resolution speed by eliminating manual analysis delays.
Solution Approach 2:
The incident prediction engine operates continuously, constantly monitoring log data and updating incident probability assessments in real-time. Unlike manual analysis that occurs intermittently when issues arise, the automated system maintains continuous surveillance, ensuring that incidents are detected and predicted without interruption, thereby improving both reliability and speed of incident response.
Data Source
AI summary
Systems and methods for predicting and preventing system incidents such as outages or failures based on advanced log analytics are described. A processing center comprising an incident prediction server and log database may receive application server logs generated by an application server and historical incident data generated by an incident database server. The processing center may be configured to cluster a subset of application server logs and based on the subset of application server logs and the incident data, determine in real time or near real time the likelihood of occurrence of an incident such as a system outage or failure.