Predictive Learning for System Update Risk Assessment
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Solution Overview
Problem
Applying updates to computing systems often results in temporary outages and undesired changes to the system's operation or performance, forcing users and administrators to choose between updating or using outdated software.
Innovation Solution
A computer-implemented method using predictive learning to evaluate updates by receiving a request, obtaining the current configuration, identifying required changes, obtaining performance data, calculating a confidence score, and providing it to the system, thereby aiding in assessing the risks associated with the update.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If updates are applied to computing system components, then system performance and security are improved, but temporary system outages and undesired changes occur
Solution Approach 1:
The system performs preliminary evaluation of updates by obtaining performance data from a data repository before applying changes. The predictive learning model assesses potential impacts in advance, allowing administrators to understand expected outcomes before deployment and schedule updates during appropriate maintenance windows to minimize outages.
Solution Approach 2:
The system uses feedback from performance data collected from multiple computing systems in the data repository. This historical performance information is fed into the predictive learning model to continuously improve its accuracy in predicting update impacts, enabling better decision-making about when and how to apply updates.
2Reliability
If updates are applied to computing system components, then system performance and security are improved, but undesired changes to operation occur
Solution Approach 1:
The system performs preliminary evaluation of updates by obtaining performance data from a data repository before applying changes. The predictive learning model assesses potential impacts in advance, allowing administrators to understand expected outcomes before deployment and schedule updates during appropriate maintenance windows to minimize outages.
Solution Approach 2:
The predictive learning model acts as an intermediary between the update application process and the computing system. It analyzes performance data and predicts potential undesired changes before updates are applied, providing administrators with confidence scores and risk assessments that mediate the decision-making process.
3Stability of the object's composition
If users continue to use outdated software, then system stability is maintained, but security vulnerabilities and performance degradation occur
Solution Approach 1:
The system uses feedback from performance data collected from multiple computing systems in the data repository. This historical performance information is fed into the predictive learning model to continuously improve its accuracy in predicting update impacts, enabling better decision-making about when and how to apply updates.
Data Source
AI summary
According to an aspect, a computer-implemented method includes receiving a request to evaluate an update to a computing system and obtaining a current configuration of the computing system. Aspects also include identifying one or more changes that the update will require to the current configuration and obtaining performance data corresponding to the one or more changes from a data repository. Aspects further include calculating a confidence score for the update based on the performance data and providing the computing system with the confidence score.