Context-Based Log Anomaly Detection Using Operational State Segmentation
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Solution Overview
Problem
Existing data processing systems face challenges in predicting and mitigating future undesired operations due to the lack of context in log messages, which can lead to impaired performance and infrastructure issues.
Innovation Solution
The implementation of a data processing system manager that uses machine learning to analyze logs and contextualize log messages by defining operational states, allowing for the identification of anomalous log segments and initiating remediation actions based on trained inference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If log messages are analyzed without context, then analysis speed is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent segments log analysis by dividing it into multiple contextual layers: individual log message level, sequence level (multiple messages), and time-series level (operational states). This segmentation allows the system to process each layer with appropriate depth, maintaining speed at the message level while achieving accuracy through aggregated contextual analysis across layers.
Solution Approach 2:
The patent adds temporal and contextual dimensions to log analysis by analyzing sequences of log messages and operational states over time. Instead of analyzing isolated messages in a single dimension, the system incorporates time-series data and contextual relationships, transforming the analysis from one-dimensional to multi-dimensional, thereby improving prediction accuracy without sacrificing speed.
2Measurement precision
If contextual information is added to log analysis, then prediction accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the complex contextual analysis task into manageable components: operational state determination, sequence generation, and anomaly detection. Each component handles a specific aspect of contextual analysis, reducing overall system complexity while maintaining comprehensive contextual coverage for accurate predictions.
Solution Approach 2:
The patent introduces an intermediary operational state representation that bridges raw log messages and anomaly detection. This intermediary layer processes and structures contextual information from multiple log messages, serving as a mediator that simplifies the complexity of direct multi-message analysis while preserving prediction accuracy.
3Reliability
If comprehensive log analysis is performed, then system reliability is improved, but processing time deteriorates
Solution Approach 1:
The patent implements periodic action by analyzing log messages in sequences and operational states rather than continuously analyzing every single message in real-time. This periodic approach processes contextual information at strategic intervals, maintaining system reliability through comprehensive analysis while reducing overall processing time by avoiding constant full-depth analysis.
Solution Approach 2:
The patent performs preliminary action by pre-processing log messages into standardized formats and pre-determining operational states before anomaly detection. This preliminary structuring of data reduces the processing time required for comprehensive analysis while maintaining the thoroughness needed for high system reliability.
Data Source
AI summary
Methods and systems for managing data processing systems based on indications of anomalous behaviors in logs are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system that reflect the historical and/or current operation of these components. The logs may be used to identify the operational state of the data processing system, improving the context of the log information. Based on the identified state, inference models may be implemented to predict future infrastructure issues by detecting anomalous behaviors of the data processing system (e.g., behaviors that are unusual for the given state) recorded in the logs. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.