Predicting Patching Automation Failures Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current patching automation processes are labor-intensive and inefficient, requiring manual intervention for a significant number of servers that fail or cannot report success, leading to time-consuming and costly manual remediation.
Innovation Solution
A system and method that utilize historical data and machine learning algorithms to predict patching automation failures by identifying key features, comparing prediction algorithms for accuracy, and suggesting corrective actions to prevent failures, thereby enhancing automation and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to remediate patching failures, then individual servers can be fixed, but labor costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of historical patching data to identify servers likely to fail before actual patching occurs. By predicting failures in advance, the system enables proactive remediation planning, reducing the need for urgent manual intervention and associated time losses.
Solution Approach 2:
The system continuously collects feedback from historical patching outcomes and uses this data to refine prediction algorithms. This feedback loop improves prediction accuracy over time, enabling more effective prioritization of manual intervention efforts and reducing overall remediation time.
2Reliability
If comprehensive manual investigation is performed on all failing servers, then patching success can be improved, but labor intensity increases
Solution Approach 1:
The system applies different levels of investigation intensity to different servers based on their predicted failure probability. High-risk servers receive comprehensive manual investigation, while low-risk servers receive minimal or no manual intervention. This localized approach improves overall patching success while reducing total manual labor required.
Solution Approach 2:
The system enables automated self-diagnosis and self-remediation for servers with predictable failure patterns. By identifying and correcting common issues automatically without human intervention, the system reduces the burden of manual operation while maintaining high patching success rates.
3Productivity
If prediction algorithms are developed to identify failing servers, then manual intervention can be optimized, but system complexity increases
Solution Approach 1:
The prediction system is divided into modular components: data collection modules, feature extraction modules, prediction algorithm modules, and remediation recommendation modules. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The prediction system is designed to handle multiple types of patching scenarios and failure modes through a unified framework. The same core prediction algorithms can be applied across different server types and operating systems, reducing complexity compared to having separate systems for each scenario.
Data Source
AI summary
An embodiment of the present invention is directed to evaluating and identifying optimal features to address and improve automation patching success. An embodiment of the present invention compares machine leaning algorithms and their accuracy in predicting the outcome of upcoming scheduled maintenance activities. Understanding that predicted outcome and the path that is generated to reach that outcome, the features that predispose an asset into a failure state can be addressed preemptively.