Dynamic Failure Rate Prediction for Computational Resources
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Solution Overview
Problem
Current systems lack dynamic and accurate failure rate predictions, relying on static estimates that do not account for changing configurations and operational histories, leading to suboptimal redundancy and service availability decisions.
Innovation Solution
A method to determine dynamic predicted failure rates by storing and analyzing failure rate information and configuration dependencies across computational resources, using a box manager to generate and update failure rates based on current system configurations and operational histories, and utilizing fault zones to ensure no single point of failure can bring down more than one zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static failure rate estimates are used based on product design time data, then the assessment is simple and quick, but the accuracy and reliability of failure rate predictions deteriorate due to not reflecting current system configurations and operational histories
Solution Approach 1:
The patent implements dynamic failure rate prediction by continuously monitoring system configuration changes and operational histories, updating failure rate estimates in real-time rather than using static design-time values. The system tracks component additions, removals, and configuration modifications to recalculate failure rates dynamically, ensuring predictions reflect current system state and operational conditions.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring actual system operation and using observed failure patterns to refine failure rate predictions. Operational histories and configuration data are fed back into the prediction model, allowing continuous improvement of accuracy based on real-world performance data rather than relying solely on theoretical design parameters.
2Reliability
If redundancy is increased to improve service availability, then reliability improves, but system expense and complexity increase
Solution Approach 1:
The system dynamically adjusts redundancy parameters based on calculated failure rates and service level requirements. By changing the failure rate parameter from static to dynamic, the system can optimize redundancy levels - adding redundancy only where and when failure rates indicate it's necessary, rather than applying uniform redundancy across all components.
Solution Approach 2:
The patent applies redundancy selectively based on local failure rate characteristics of individual components or subsystems. Instead of uniform redundancy, the system identifies specific areas where failure rates are high or where configuration changes have increased vulnerability, and applies redundancy locally to those areas, optimizing overall system reliability while minimizing total redundancy overhead.
3Measurement precision
If dynamic failure rate prediction is implemented to improve accuracy, then service provisioning decisions improve, but computational resources and processing time increase
Solution Approach 1:
The system implements partial monitoring and prediction by focusing computational resources on critical components or subsystems where failure rate changes have the greatest impact on service availability. Rather than continuously analyzing every component in the system, the patent applies dynamic prediction selectively to components that are most influential to overall system reliability or that have recently undergone configuration changes.
Data Source
AI summary
A method and apparatus for determining predicted failure rates for computational resources provided by a system comprising multiple components. The method comprises storing failure rate information about individual components and determining a current configuration of components within the system. The method further comprises identifying from the current configuration dependencies between components within the system. The stored failure rate information and the identified current configuration dependencies can then be used to generate predicted failure rates for the computational resources in the current configuration of the system.