Compute Node Update Duration Prediction Model
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Solution Overview
Problem
Current methods for predicting the duration of updates in large-scale computer deployments are inaccurate, leading to difficulties in scheduling updates and minimizing downtime, as administrators rely on manual estimates based on previous update times.
Innovation Solution
A duration prediction model is trained using historical data from previous updates, incorporating features such as update components, compute node attributes, and machine learning algorithms like XGBRegressor to estimate the time required for future updates, allowing for more precise scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual estimation methods are used to predict update duration, then the scheduling process is simple, but the prediction accuracy is poor
Solution Approach 1:
The patent replaces manual estimation methods with an automated machine learning prediction system. The ML model automatically analyzes historical update data, compute node attributes, and update package characteristics to generate accurate duration predictions, eliminating the need for administrator manual estimation while significantly improving prediction accuracy.
Solution Approach 2:
The prediction system is designed to be self-learning and self-improving through automated ML algorithms that continuously analyze historical data patterns. The system automatically trains on past update performance data and computes predictions without requiring manual intervention, making the complex prediction process autonomous while maintaining high accuracy.
2Loss of time
If updates are scheduled without accurate duration predictions, then scheduling is flexible, but compute node downtime cannot be minimized
Solution Approach 1:
The patent performs update duration prediction before the actual update is executed. By using the ML model to forecast the required maintenance time window in advance, administrators can schedule updates during optimal periods, thereby minimizing the impact on compute node availability and reducing overall downtime.
Solution Approach 2:
The system incorporates feedback mechanisms where actual update performance data is fed back into the ML model to continuously improve prediction accuracy. This closed-loop approach ensures that the prediction system learns from real-world outcomes and becomes increasingly accurate at minimizing downtime through better scheduling decisions.
3Productivity
If multiple updates are deployed simultaneously to many compute nodes, then productivity is high, but the impact of downtime is amplified
Solution Approach 1:
The patent segments the update deployment process by predicting durations for individual compute nodes or groups, then using these predictions to create an optimized rollout schedule. This allows updates to be deployed in controlled waves rather than all at once, maintaining high productivity while preventing the amplification of downtime impact across the entire infrastructure.
Solution Approach 2:
The system dynamically adjusts update deployment strategies based on predicted durations and actual performance feedback. The ML model enables real-time decision-making about which compute nodes should receive updates next, optimizing the balance between deployment speed and minimizing total downtime impact across the infrastructure.
Data Source
AI summary
A device and corresponding method are provided to provide accurate estimates of how long it will take to install updates to compute nodes in a large-scale computer deployment. a duration prediction model is trained using historical data from previous updates to compute nodes. The features selected to train the duration prediction model are update features including update component type, update component size, update component duration and compute node features including operating system, BMC type/version, UEFI type/version, and generation for each of the compute nodes updated. The historical data for the features is accessed from a metadata store.