Graph Embeddings for Scalable Configuration Drift Detection
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Solution Overview
Problem
Existing technologies face challenges in efficiently detecting configuration drift in large-scale computer hardware systems due to scalability issues and the inability to handle encrypted configurations, as they rely on syntactic comparisons and specialized training data, making them non-generalizable and inefficient for comparing configurations across different products/releases.
Innovation Solution
The use of graph embeddings, specifically through Graph Isomorphism Networks (GIN), to model configuration dependencies as graphs, allowing for efficient comparison and detection of configuration drift by transforming variables into embeddings that can handle varying node numbers and non-static node orderings, using a GIN model to enhance the Weisfeiler-Lehman kernel for scalable and continuous evolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If syntactic comparisons and specialized training data are used for configuration drift detection, then detection accuracy for specific configurations can be achieved, but scalability and generalizability across different products/releases deteriorate
Solution Approach 1:
The patent transforms configuration data from syntactic representations to graph embeddings, changing the parameter representation method. This allows the system to maintain detection accuracy while gaining scalability, as graph embeddings capture structural relationships that are product-agnostic and can be applied across different hardware configurations without retraining on specialized data
Solution Approach 2:
The patent replaces traditional syntactic comparison mechanisms with graph isomorphism testing. Instead of comparing configuration strings or tables using specialized algorithms, the system converts configurations to graph structures and uses graph neural networks to detect drift, enabling generalization across different product lines while maintaining precision
2Ease of manufacture
If traditional configuration comparison methods are used, then implementation simplicity can be maintained, but scalability to large-scale hardware systems deteriorates
Solution Approach 1:
The patent introduces graph embeddings as an intermediary representation between raw configuration data and drift detection analysis. This intermediary layer simplifies the comparison process by converting complex configuration structures into standardized graph representations that can be efficiently processed at scale using graph neural networks
Solution Approach 2:
The patent segments configuration data into discrete graph components (nodes representing configuration elements and edges representing relationships). This segmentation enables parallel processing and efficient comparison of large-scale hardware configurations, improving scalability while maintaining implementation feasibility through modular graph processing
3Adaptability or versatility
If configurations are represented with varying node numbers and non-static node orderings, then flexibility in representing different hardware states is improved, but comparison complexity and drift detection difficulty increase
Solution Approach 1:
The patent leverages the asymmetric nature of graph isomorphism testing, where the graph structure itself encodes the relationship between nodes regardless of their original ordering. This allows flexible representation of hardware states with varying nodes and orderings while maintaining efficient comparison through structure-based isomorphism testing that is invariant to node labeling
Data Source
AI summary
A system can create a first graph that represents first values of current configurations of a computer hardware as first nodes and first dependencies of the current configurations as first links. The system can create first embedding vectors based on the first graph. The system can input the first embedding vectors to a graph isomorphism network to produce modified first embedding vectors. The system can identify specified configurations for the computer hardware. The system can create a second graph that represents second values of the specified configurations. The system can create second embedding vectors based on the second graph. The system can input the second embedding vectors to the graph isomorphism network to produce modified second embedding vectors. The system can perform a graph isomorphism evaluation on the modified first embedding vectors and the modified second embedding vectors to determine an amount of difference.