Steady-State Log Analyzer for Domain-Agnostic Outlier Detection
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Solution Overview
Problem
Conventional approaches to analyzing and diagnosing issues in cloud computing services are time-consuming and require domain-specific knowledge, often involving trial and error, as log files can be thousands of lines long and identifying the primary cause of service interruptions is challenging.
Innovation Solution
A domain-agnostic outlier detection model is trained to predict whether portions of a log file correspond to normal or non-normal service behavior by generating a multi-dimensional representation of the log file, reducing dimensionality, and applying an autoencoder or PCA engine to identify outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of log files is performed by domain experts, then diagnostic accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automated self-diagnosis of log files through machine learning models that independently analyze and identify anomalies without requiring manual domain expert intervention. The outlier detection model processes log files autonomously, extracting patterns and identifying issues that would otherwise require human analysis.
Solution Approach 2:
The patent replaces the mechanical process of manual log file inspection with an automated computational system. Machine learning models and outlier detection algorithms substitute human experts, transforming the diagnostic process from manual reading and analysis to automated pattern recognition and anomaly detection.
2Measurement precision
If manual analysis of thousands of log lines is performed, then diagnostic accuracy is improved, but productivity decreases
Solution Approach 1:
The system performs automated self-analysis of log files through trained machine learning models that process thousands of log lines independently. The outlier detection model automatically identifies anomalies and generates diagnostic results without human intervention, enabling high-volume processing while maintaining accuracy.
Solution Approach 2:
The patent transforms the analysis approach by changing from manual line-by-line inspection to automated statistical and pattern-based analysis. Machine learning models process log data using learned parameters and patterns, enabling rapid analysis of large volumes of log files while maintaining diagnostic precision.
3Measurement precision
If domain-specific knowledge is required for log analysis, then diagnostic accuracy is improved, but adaptability to different services decreases
Solution Approach 1:
The patent creates a universal outlier detection model that can analyze log files across multiple different services and domains. The machine learning framework is designed to be service-agnostic, learning patterns from diverse log data sources and applying the same analytical approach to different types of services without requiring service-specific customization.
Solution Approach 2:
The system enables services to perform self-diagnosis through a universal analytical framework. The outlier detection model adapts to different service log formats and patterns automatically through its learning capability, eliminating the need for domain experts to have service-specific knowledge while maintaining diagnostic accuracy across diverse services.
4Reliability
If trial and error approaches are used to identify service issues, then diagnostic thoroughness is improved, but time consumption and productivity worsen
Solution Approach 1:
The patent implements preliminary analysis through automated outlier detection that identifies potential issues before they escalate into major problems. The machine learning model continuously monitors log files and flags anomalies early, enabling proactive intervention rather than reactive trial-and-error debugging after service failures occur.
Solution Approach 2:
The system replaces trial-and-error diagnostic approaches with systematic automated analysis. Machine learning models provide structured, consistent evaluation of log data, identifying root causes directly through pattern recognition rather than requiring iterative hypothesis testing and experimentation.
Data Source
AI summary
The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.