Rolling-Window Metadata Anomaly Detection for Long Log Contexts
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Solution Overview
Problem
Conventional anomaly detection models struggle to identify anomalous sub-sequences in system logs and detect root causes effectively, particularly in scenarios where the input context length exceeds the model's capacity, and they lack the ability to dynamically adjust their input context window.
Innovation Solution
A framework leveraging rolling windows and large language models (LLMs) to dynamically adjust input context length, detect anomalous sub-sequences, and perform root cause analysis in system logs by leveraging contextual and syntactic learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anomaly detection models are used with fixed input context length, then the model structure is simple, but the model cannot effectively detect anomalies when input context length exceeds model capacity
Solution Approach 1:
The patent divides the input log into multiple overlapping windows of size w, where each window is processed independently by the LLM. This segmentation allows the model to handle long context logs by processing them in manageable chunks, resolving the contradiction between handling long context and maintaining simple model structure.
Solution Approach 2:
The patent implements a dynamic window sliding mechanism that moves the window across the log sequence. The window position is dynamically adjusted to create overlapping regions, allowing the model to adaptively process different portions of the log. This dynamic approach enables effective anomaly detection in long context without requiring a fundamentally complex model architecture.
2Adaptability or versatility
If the input context window is fixed, then the system is simple to implement, but it cannot dynamically adapt to different log lengths and complexity levels
Solution Approach 1:
The patent implements a dynamic window sliding mechanism that automatically adapts to different log lengths. The window position is dynamically adjusted based on the log content, allowing the system to handle variable context lengths without requiring complex reconfiguration. This dynamic adaptation resolves the contradiction between versatility and implementation complexity.
Solution Approach 2:
The patent changes the window size parameter w to control the balance between context detail and processing efficiency. By adjusting this single parameter, the system can adapt to different log lengths and complexity levels without requiring fundamental changes to the architecture. This parameter-based adaptation resolves the contradiction between versatility and complexity.
3Measurement precision
If rolling windows with overlapping regions are used, then anomaly detection precision is improved through contextual analysis, but processing time increases
Solution Approach 1:
The patent segments the log into overlapping windows, where each window is processed independently and in parallel. This segmentation enables precise contextual analysis while reducing processing time through parallelization, as multiple windows can be analyzed simultaneously without requiring sequential processing of the entire log.
Solution Approach 2:
The patent uses overlapping windows that extend beyond the minimum required context, providing excessive contextual information to improve detection precision. However, this is managed by processing only the necessary portions of the log through the windowing mechanism, balancing precision improvement with acceptable processing time through selective analysis of critical regions.
Data Source
AI summary
A method for managing an anomaly in a client includes: obtaining, by an analyzer, historical metadata (HM); obtaining, by the analyzer, an error description that is associated with the HM; analyzing, by the analyzer, the HM to generate a first data frame (DF); generating, by the analyzer, a second DF and a third DF based on the first DF, in which the second DF and the third DF are sent to an engine; generating, by the analyzer, a fourth DF based on the first DF and error description, in which the fourth DF is sent to the engine; tuning, by the engine, an anomaly detection model (ADM) to obtain a tuned ADM using: a first target parameter (TP) and the second DF; a second TP and the third DF; a third TP and the fourth DF; and initiating, by the engine, notification of an administrator about the tuned ADM.