Contrastive Neural Network Training for Reduced-Label Log Analysis
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Solution Overview
Problem
Existing log analysis techniques face challenges due to non-standardized recording practices across different systems, leading to inefficiencies in automating parsing and analyzing logs, which requires significant memory, time, and computing resources, and are limited by the need for task-specific training data and labeled datasets.
Innovation Solution
A neural network training method using contrastive learning and similarity loss to encode log messages without task-specific labels, allowing for generalized encoding across various tasks, combined with telemetry data for enhanced anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log analysis techniques are used, then comprehensive log parsing and analysis can be achieved, but significant memory, time, and computing resources are required
Solution Approach 1:
The patent changes the parameter of training data representation by using contrastive learning with positive and negative pairs instead of traditional labeled classification data. This approach reduces the computational complexity and resource requirements while maintaining effective log analysis capabilities through similarity-based embedding learning.
Solution Approach 2:
The system performs self-service learning by automatically generating positive pairs (semantically similar logs) and negative pairs (semantically different logs) without requiring manual labeling. The contrastive learning framework enables the model to learn meaningful representations autonomously, reducing the need for extensive human-annotated training data and associated computational overhead.
2Reliability
If task-specific training data and labeled datasets are used, then accurate classification can be achieved, but the system is limited and requires significant resources
Solution Approach 1:
The patent implements a universal log analysis framework using contrastive learning that can handle multiple log analysis tasks simultaneously. By learning general-purpose embeddings from positive and negative pairs, the system creates a multi-functional model that can be applied to various classification and analysis tasks without requiring separate task-specific training data, thereby reducing overall system complexity.
Solution Approach 2:
The patent introduces contrastive learning as an intermediary training approach between raw log data and final classification tasks. This intermediary layer learns meaningful representations through positive and negative pair comparisons, serving as a bridge that enables accurate classification across multiple tasks without requiring direct task-specific labeled data for each application.
3Loss of information
If automated parsing and analysis of logs is performed, then information extraction and detection can be achieved, but significant memory and time resources are consumed
Solution Approach 1:
The patent performs preliminary action by pre-training the model using contrastive learning on positive and negative log pairs to learn meaningful embeddings before actual log analysis tasks. This preliminary training phase creates a robust foundation that enables faster and more efficient information extraction during deployment, reducing the time required for automated parsing and analysis of production logs.
Solution Approach 2:
The patent replaces traditional mechanical log parsing and analysis systems with a contrastive learning-based neural network approach. Instead of using rule-based or template-matching methods that require significant computational resources, the system uses learned embeddings and similarity comparisons to efficiently extract and detect information from logs, thereby reducing both memory and time requirements.
Data Source
AI summary
Methods, systems, and machine-readable mediums to encode at least one vector associated with a log using a neural network. In at least one embodiment, a neural network is trained, at least in part, by obtaining first, second, and third encoded vectors by encoding a first vector associated with a first log sequence, a second vector associated with a second log sequence similar to the first log sequence, and a third vector associated with a third log sequence dissimilar from the first log sequence; and selecting at least one model weight that increases a likelihood that the first encoded vector is closer to the second encoded vector than the third encoded vector.