Dynamic Upgrade Prediction for Multi-Component Products
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Solution Overview
Problem
Multi-component products face challenges in predicting overall upgrade time due to varying component deployment and stage weight comparisons, leading to unreliable upgrade time estimates.
Innovation Solution
A dynamic upgrade prediction method is implemented, where a centralized management node generates an initial prediction based on a subset of component nodes, performs real-time progress reviews, and updates the prediction using equations that adjust for elapsed time, allowing for dynamic increases in upgrade duration when stages take longer than expected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predefined total upgrade time with fixed percentages for each stage is used, then the upgrade process can be planned and tracked, but the prediction becomes unreliable for multi-component products with varying component deployment and stage weights
Solution Approach 1:
The patent applies dynamics by making the upgrade prediction adaptive rather than static. The system continuously monitors real-time progress of each component upgrade and dynamically adjusts the overall upgrade time prediction based on actual performance. This allows the prediction to adapt to varying component deployment scenarios and stage weights, resolving the contradiction between prediction accuracy and product complexity.
Solution Approach 2:
The patent implements feedback mechanisms by comparing real-time upgrade progress against the initial prediction and using this information to update the overall upgrade time estimate. The system collects feedback from each component's upgrade status and stage completion, then feeds this information back into the prediction model to improve accuracy for multi-component products.
2Measurement precision
If real-time progress review and dynamic updates are implemented for each component node, then upgrade time prediction accuracy improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the multi-component product upgrade process into independent component nodes, each with their own upgrade stages. The centralized management node processes each component's progress separately, calculating individual component upgrade times and then aggregating these to determine the overall prediction. This segmentation reduces the computational burden on the management node while maintaining prediction accuracy.
3Reliability
If the upgrade prediction is dynamically increased when stages take longer than expected, then the prediction remains reliable, but the overall upgrade duration extends beyond initial estimates
Solution Approach 1:
The patent uses feedback to monitor real-time upgrade progress and compare it against the initial prediction. When actual progress deviates from expectations, the system updates the prediction to reflect the new reality. This feedback mechanism maintains prediction reliability by ensuring the estimate always reflects current performance, even if it means the total duration extends beyond the initial estimate.
Solution Approach 2:
The patent applies dynamics by allowing the upgrade prediction to be flexible and adaptive rather than fixed. The system dynamically adjusts the overall upgrade time based on actual component performance, ensuring the prediction remains reliable and accurate throughout the upgrade process rather than being constrained by an initial static estimate.
Data Source
AI summary
Techniques are disclosed for generating a dynamic upgrade prediction. The prediction includes generating an initial upgrade prediction for an upgrade to be performed on a subset of component nodes; performing a real time progress review, using a centralized management node, of the upgrade, wherein each of the component nodes includes a number of stages; performing an upgrade duration comparison between the initial upgrade prediction and a real time upgrade time for a subset of the stages; and generating an updated upgrade prediction for the multi-component product upgrade based on the comparison between the initial upgrade prediction and the real time upgrade time.