Computer Module Ranking for Service Continuity
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Solution Overview
Problem
Computer systems face challenges in optimizing resource allocation and minimizing failure impacts due to the varying importance of services and computer modules, where some modules support multiple services and failures can lead to significant disruptions.
Innovation Solution
A computing system ranks computer modules based on their importance scores, which are calculated from the services they support, allowing for prioritization and allocation of redundant resources to critical modules, thereby minimizing disruptions and optimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If back-up computer modules are provided for all services, then reliability is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by differentiating the level of redundancy provided to different computer modules based on their importance scores. Critical modules (high importance) receive full backup redundancy, while less critical modules (low importance) receive reduced or no redundancy. This resolves the contradiction by optimizing resource allocation to match actual service criticality needs.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the redundancy level based on the importance score parameter. The importance score is calculated from service dependencies and failure impacts, and this parameter determines the backup allocation strategy. This resolves the contradiction by making redundancy adaptive rather than uniform.
2Reliability
If computer modules supporting multiple services are monitored closely, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by creating a unified monitoring framework that handles both single-service and multi-service modules through the same importance score calculation mechanism. The monitoring system uses a universal algorithm that automatically determines criticality based on service dependencies, resolving the contradiction by avoiding separate complex monitoring systems for different module types.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting monitoring intensity based on the importance score parameter. Modules with higher importance scores receive more intensive monitoring, while lower importance modules receive standard monitoring. This resolves the contradiction by making monitoring complexity adaptive to actual risk levels.
3Reliability
If redundant resources are allocated to critical modules, then reliability is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by concentrating redundant resources specifically on modules with high importance scores (critical modules) while providing minimal or no redundancy to low importance modules. This resolves the contradiction by optimizing the distribution of redundant resources to match actual service criticality, ensuring critical services have adequate backup while non-critical services consume fewer resources.
Solution Approach 2:
The patent uses parameter changes by making redundancy allocation dynamic based on the importance score parameter. The importance score is calculated from service dependencies, failure impacts, and operational criticality, and this parameter directly determines the level of redundant resource allocation. This resolves the contradiction by making redundancy adaptive rather than uniform across all modules.
Data Source
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AI summary
A non-transitory computer-readable storage medium may include instructions stored thereon for ranking multiple computer modules to reduce failure impacts. When executed by at least one processor, the instructions may be configured to cause a computing system implementing the multiple computer modules to at least associate the multiple computer modules with multiple services that rely on the multiple computer modules, at least one of the multiple services relying on more than one of the multiple computer modules, determine values of the multiple services, and rank the multiple computer modules based on the determined values of the multiple services with which the respective multiple computer modules are associated.