Log Representation Learning for Cloud System Maintenance
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Solution Overview
Problem
Current cloud systems face challenges in diagnosing system failures due to the vast amount of data generated, requiring significant time and resources to identify and prevent issues, and existing anomaly analysis models overlook the information in system logs, leading to suboptimal representation learning and difficulty in aligning representations across diverse entities.
Innovation Solution
A domain-specific language model-based log representation learning technique is developed, which transforms raw system log data into high-quality representations using an optimized parser, tokenizer, and deep learning, incorporating domain-specific golden signals and machine learning approaches to extract anomaly scores, and aligning representations across entities with padding to manage diverse time scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current cloud systems produce enormous amounts of data from numerous computing nodes, then system robustness and scalability are improved, but the difficulty of diagnosing system failures increases and more time and resources are required
Solution Approach 1:
The patent extracts only the relevant information from system logs using an optimized parser that transforms collected system logs into log templates. This extraction process filters out unnecessary data while preserving critical failure information, enabling effective diagnosis without processing the entire enormous dataset.
Solution Approach 2:
The patent introduces an intermediary representation layer by tokenizing log templates into log template tokens and training a language model to generate representations. This intermediary representation transforms raw log data into a compressed, meaningful format that captures essential patterns, making failure diagnosis feasible despite the volume of original data.
2Measurement precision
If an optimized parser and language model are used to transform system logs into representations, then the accuracy of failure diagnosis is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the log processing pipeline into distinct functional components: an optimized parser for transforming logs into templates, a tokenizer for dividing templates into time windowed tokens, and a language model for generating representations. This segmentation allows each component to be optimized independently while working together to achieve high diagnostic accuracy.
Solution Approach 2:
The patent changes the representation parameters by training the language model to generate compressed representations of log sequences. This parameter transformation converts raw log data into a lower-dimensional representation space that preserves diagnostic information while reducing complexity for analysis.
3Speed
If existing anomaly analysis models are used, then the processing speed is maintained, but the representation learning quality deteriorates due to overlooking information in system logs
Solution Approach 1:
The patent maintains continuous processing of system logs through the optimized parser and language model pipeline. The system continuously transforms new log data into representations in real-time, ensuring both speed and quality by never stopping the useful action of converting raw logs into meaningful representations.
Solution Approach 2:
The patent implements feedback mechanisms where the language model is trained on log template tokens and can generate representations that are evaluated for quality. This feedback loop allows the system to improve representation learning quality while maintaining processing speed through iterative optimization.
Data Source
AI summary
Systems and methods for log representation learning for automated system maintenance. An optimized parser can transform collected system logs into log templates. A tokenizer can tokenize the log templates partitioned into time windows to obtain log template tokens. The log template tokens can train a language model (LM) with deep learning to obtain a trained LM. The trained LM can detect anomalies from system logs to obtain detected anomalies. A corrective action can be performed on a monitored entity based on the detected anomalies.