Software Framework Monitoring with Machine-Learning Incident Signatures
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Solution Overview
Problem
Existing methods for monitoring complex software application frameworks are inefficient and unreliable due to the large number of interdependent services and microservices, leading to inefficient processing of software incident data objects and unnecessary computational resources.
Innovation Solution
The use of machine-learning-based techniques to generate incident signatures for software application frameworks, including natural language processing and structured data processing models, to determine predicted incident severity levels and direct software incident data objects to appropriate resolution agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional monitoring methods are used for complex software application frameworks, then the system can process all incident data, but the processing efficiency is low and computational resources are wasted
Solution Approach 1:
The patent segments incident data into two distinct categories: structured data (metadata, logs, metrics) and unstructured data (descriptions, comments). Different machine learning models are applied to each type - structured data processing models for structured fields and natural language processing models for unstructured fields. This segmentation enables efficient, targeted processing of each data type, improving overall incident processing efficiency while reducing unnecessary computational resource consumption.
2Measurement precision
If machine learning models are used to process both structured and unstructured incident data, then processing accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the incident data processing into separate structured and unstructured components, applying specialized machine learning models to each. This segmentation maintains high classification accuracy by using appropriate models for each data type while managing system complexity through modular architecture where each model handles a specific data type independently.
Solution Approach 2:
The patent employs a unified incident severity detection machine learning model that receives inputs from both structured data processing and natural language processing pipelines. This multi-functional model integrates results from different data types to produce a comprehensive severity assessment, maintaining high accuracy while consolidating the complexity into a single detection mechanism.
3Reliability
If all incident data objects are processed in detail, then complete analysis is achieved, but resolution time increases
Solution Approach 1:
The system applies different levels of processing depth to different incident data types. Structured data undergoes systematic extraction and analysis, while unstructured data receives natural language processing. The incident severity detection model synthesizes these partial analyses to achieve reliable overall assessment without requiring exhaustive detailed analysis of every single data field, thereby reducing resolution time while maintaining analysis completeness.
Data Source
AI summary
Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, and an incident severity level detection machine learning model.