Automated Multi-Cluster Management for Version Control
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Solution Overview
Problem
Current methods for managing updates and version control adopt a one-size-fits-all approach, failing to consider the specific usage and potential impact on target systems, which can lead to overtaxing hardware, software reliability issues, and communication disruptions.
Innovation Solution
An automated multi-cluster management apparatus that interfaces with remote clusters to provide data-driven, cluster-specific version/update control by collecting and analyzing software, hardware, and cluster requirement data against multi-cluster data, ranking solutions based on metrics to select optimal updates and schedules them for distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a one-size-fits-all update approach is used to bring all systems to the latest version, then version uniformity is improved, but system reliability and hardware compatibility deteriorate
Solution Approach 1:
The patent applies local quality by customizing update decisions for each target system based on its specific characteristics. The system collects data about individual system hardware, software, and usage patterns, then uses this localized information to determine which updates are appropriate for each specific system rather than applying uniform updates to all systems.
Solution Approach 2:
The patent changes the parameter of update selection from a fixed uniform approach to a dynamic parameter-based approach. Multiple parameters including hardware specifications, software versions, usage patterns, and priority levels are considered and weighted to determine the optimal update decision for each target system.
2Loss of time
If updates are applied to all systems regardless of specific conditions, then version currency is improved, but hardware performance deteriorates
Solution Approach 1:
The patent applies partial action by selectively applying updates only to those target systems that can benefit from them based on their specific conditions. Instead of applying all available updates to all systems, the system evaluates each target system individually and applies only the appropriate subset of updates, avoiding unnecessary hardware strain on systems where updates are not beneficial.
3Measurement precision
If comprehensive data collection and analysis is performed for each cluster, then update accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex update management process into distinct functional modules: data collection module, data processing module, update determination module, and update application module. Each module handles a specific aspect of the process, making the overall complex system more manageable and maintainable while still achieving comprehensive data analysis for accurate update decisions.
Data Source
AI summary
A method and apparatus for data driven and cluster specific version/update control. The apparatus includes an automated multi-clusters management apparatus that interfaces with a plurality of remote clusters to provide data driven version/update control on a cluster by cluster basis. Generally, operation includes collection/identification of cluster specific data pertaining to software, hardware, and cluster requirements. The cluster specific data is later compared/analyzed against multi-cluster data pertaining to software releases, hardware characteristics, and known bugs/issues for each. The results of the comparison/analysis can then be ranked according to various metrics to different possible solutions and to differentiate the less desirable results from the more desirable results. Thus, the automated multi-cluster management apparatus provides for selection of versions/updates that is dependent on the cluster specific data. Additionally, the present disclosure provides for scheduling and distribution planning for selected versions/updates.