Upgrade Failure Prediction System Using Live Data Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face inefficiencies in managing and predicting upgrade failures of client components, leading to potential downtime and resource wastage, as they lack proactive mechanisms to determine whether user intervention is required for resolving upgrade issues.
Innovation Solution
A system that generates upgrade failure predictions by processing raw training data into clustered data, using a prediction model to identify relevant features, and initiating actions based on the likelihood of user involvement needed to resolve upgrade failures, thereby reducing the impact of such failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive upgrade failure prediction is implemented, then system reliability is improved, but device complexity increases
Solution Approach 1:
The prediction system is segmented into distinct functional modules: data collection module, feature extraction module, prediction model module, and action initiation module. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while achieving reliable upgrade failure prediction
Solution Approach 2:
A recommendation system acts as an intermediary between the upgrade process and user intervention. The system processes upgrade data, generates failure predictions, and initiates appropriate actions without requiring direct user involvement in the prediction process, thereby improving reliability while managing complexity through automation
2Measurement precision
If comprehensive data processing and analysis are performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by collecting and processing upgrade data in advance, building prediction models before actual upgrade failures occur. This allows the system to have predictions ready when needed, improving measurement precision without incurring time delays during critical upgrade moments
Solution Approach 2:
The patent replaces manual data analysis and failure prediction with automated machine learning models and algorithms. This substitution of mechanical/manual processes with computational systems enables comprehensive data processing to achieve high prediction accuracy without proportional increases in time loss
3Productivity
If automated prediction and action initiation are implemented, then productivity is improved, but ease of operation deteriorates
Solution Approach 1:
The prediction system operates autonomously, collecting data, generating predictions, and initiating actions without requiring user intervention. This self-service capability improves productivity by automating the entire upgrade failure management process, while the complexity is managed through automated decision-making algorithms rather than requiring simple manual operation
Data Source
AI summary
A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.