Grouping Information Handling Systems for Software Updates
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Solution Overview
Problem
Information handling systems face challenges in efficiently deploying software updates due to variations in health scores, availability, and update churn, leading to potential poor user experiences and skewed testing results, especially when systems with poor health are included in initial update testing.
Innovation Solution
A method that uses machine learning processes, such as k-means clustering, density-based clustering, or autoencoders, to dynamically distribute information handling systems into groups based on performance health scores, availability scores, and update churn scores, allowing for targeted software updates and minimizing risks by initially testing updates on systems with better health metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software updates are deployed to all information handling systems simultaneously, then deployment speed is improved, but update success rate deteriorates due to variations in system health scores
Solution Approach 1:
The patent segments the information handling systems into multiple groups based on health scores, availability metrics, and update churn scores. This segmentation allows simultaneous deployment to multiple groups while maintaining reliability by ensuring each group receives updates appropriate to its health status. The machine learning model dynamically creates these segments, enabling both speed and success rate improvement.
Solution Approach 2:
The patent applies local quality by tailoring update deployment strategies to each specific group's characteristics. Instead of uniform treatment, each group receives updates based on its local health metrics and performance characteristics. This ensures that systems with better health scores receive updates first, while systems with poorer health scores are updated later or with different strategies, optimizing overall success rate.
2Quantity of substance
If information handling systems with poor health scores are included in initial update testing, then testing coverage is improved, but user experience deteriorates
Solution Approach 1:
The patent applies preliminary action by first identifying and updating systems with better health scores before attempting updates on systems with poor health scores. This staged approach ensures that initial testing and deployment occur on more stable systems, providing better user experience while still achieving comprehensive coverage through subsequent updates to previously excluded systems.
Solution Approach 2:
The patent uses dynamic group assignment where systems can move between groups based on changing health metrics. A system that starts in a later group can move to an earlier group if its health improves, and vice versa. This dynamic approach ensures comprehensive testing coverage while optimizing user experience at each moment based on current system states.
3Device complexity
If static grouping is used for software updates, then implementation complexity is reduced, but adaptability to system health changes deteriorates
Solution Approach 1:
The patent implements dynamic grouping where the machine learning model continuously monitors health metrics and reassigns systems to appropriate groups based on current conditions. This dynamic approach maintains adaptability to health changes while the automated nature of the process keeps implementation complexity manageable through standardized algorithms and automated decision-making.
Solution Approach 2:
The system performs self-service by automatically monitoring health metrics, evaluating system states, and reassigning groups without manual intervention. The machine learning model autonomously adapts to changing conditions, adjusting group assignments based on real-time data, thereby maintaining high adaptability while minimizing the complexity of manual management.
4Reliability
If machine learning processes are used to dynamically group systems, then update success rate is improved, but computational complexity increases
Solution Approach 1:
The patent replaces manual or rule-based grouping mechanisms with machine learning processes. The ML model automatically analyzes health scores, availability metrics, and update churn data to create optimal group assignments. This substitution improves update success rate through data-driven decisions while the automated nature of ML reduces the need for complex manual intervention and configuration.
Solution Approach 2:
The patent uses parameter changes by continuously monitoring and reevaluating multiple metrics (health scores, availability, update churn) to dynamically adjust group assignments. The machine learning model processes these changing parameters in real-time, improving update success rate by adapting to current system states while managing computational complexity through efficient parameter processing and model optimization.
Data Source
AI summary
In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.