Cloud Redundancy Risk Index Calculation for Failure Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing systems lack an effective method to quantify and balance redundancy across domains, making it difficult to determine the at-risk probability of services and prioritize component failures for maintenance.
Innovation Solution
A method is introduced to calculate an at-risk probability in the compute, storage, and network domains by using logarithmic risk indexing, considering factors like blade and disk failure rates, failover rates, and replacement times, allowing for a comprehensive risk assessment and prioritization of component maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional fractional threshold methods are used to measure redundancy, then the measurement is simple, but the ability to quantify and compare redundancy across different domains is insufficient
Solution Approach 1:
The patent transforms redundancy measurement from simple fractional thresholds to probabilistic risk indices by changing the parameter representation. It calculates at-risk probabilities using failure rates, replacement times, and component configurations, then normalizes these into comparable risk indices across compute, storage, and network domains, enabling precise quantitative comparison while maintaining operational simplicity.
2Speed
If redundancy is measured using simple fractional thresholds, then the measurement process is fast, but the ability to prioritize component failures for maintenance is limited
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors component health, failure rates, and redundancy status across all domains. It calculates real-time at-risk probabilities and generates prioritized failure lists based on current system state, enabling both rapid measurement and intelligent prioritization of maintenance activities based on actual risk conditions.
3Measurement precision
If comprehensive risk assessment considering multiple factors is performed, then the accuracy of service risk evaluation is improved, but the complexity of the assessment process increases
Solution Approach 1:
The patent segments the complex risk assessment into separate domain-level calculations (compute domain, storage domain, network domain), each with its own specific risk factors and formulas. This segmentation allows comprehensive multi-factor assessment while maintaining manageable complexity through modular, domain-specific evaluation processes that can be independently optimized.
Data Source
AI summary
Methods and apparatus for generating at risk probabilities for a pre-integrated cloud computing system. In one embodiment, a system determines a revised overall risk index after at least two component failures in at least two of the compute domain, storage domain, and storage paths to assist a user in selecting a first one of the at least two component failures to fix first.