Server-Based Outage Detection Using Application Event Logs
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Solution Overview
Problem
Manual review of millions of application event logs for detecting system outages is time-consuming and often ineffective, leading to prolonged downtime in critical systems like banking and e-commerce, where system availability is crucial.
Innovation Solution
A server device trained using machine learning techniques to analyze application event logs, identifying characteristics indicative of system outages or availability, allowing for real-time detection and reducing downtime by comparing current logs to trained patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of application event logs is used to detect system outages, then detection accuracy may be maintained through human analysis, but the process becomes extremely time-consuming and ineffective when millions of logs are generated daily
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated computer-based system that uses machine learning algorithms to analyze application event logs. The server automatically processes millions of logs, identifies patterns indicative of system outages, and generates alerts without human intervention, thereby eliminating the time constraint while maintaining detection accuracy through sophisticated pattern recognition algorithms.
2Extent of automation
If robot interactions are created to simulate users and detect outages, then automated detection can be achieved, but the process becomes difficult to create and maintain with errors and inaccuracies
Solution Approach 1:
The patent extracts the essential detection function from complex robot interaction simulations and implements a streamlined automated system that directly analyzes application event logs. By removing the intermediary layer of simulated user interactions, the system achieves automation while significantly reducing complexity. The machine learning model directly processes log data to identify outage patterns, eliminating the need to create, maintain, and debug complex robot interaction scenarios.
3Reliability
If system administrators manually review application data to detect outages, then comprehensive analysis can be performed, but the process is ineffective when millions of events are logged each day
Solution Approach 1:
The patent implements a self-service automated system where the computer itself performs the comprehensive analysis of application event logs without requiring human administrators to manually review data. The machine learning algorithms automatically process millions of logs daily, identify patterns indicating system outages, and generate alerts. This enables the system to maintain comprehensive analysis capability while scaling to handle vast volumes of log data that would be impossible for human administrators to review manually.
Data Source
AI summary
Methods, systems, apparatus, and non-transitory computer readable media are described for detecting system outages using application event logs. Various aspects may include obtaining several prior application event logs where the status of the system is known at the time the application event logs were recorded. Additionally, various aspects may include determining characteristics of prior application event logs which were recorded during a system outage, and/or determining characteristics of prior application event logs which were recorded while the system was available. When current application event logs are obtained where the status of the system is unknown at the time the current application event logs are recorded, various aspects include comparing the current application event logs to the prior application event logs to determine that a system outage has occurred based upon the comparison.