Hybrid IT Resilience Measurement via Component Mapping
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Solution Overview
Problem
Current systems for measuring resilience in hybrid IT infrastructure environments only consider availability and downtimes, neglecting other critical factors like monitoring status, component recoverability, and backups, and do not account for the severity and extent of impact during outages.
Innovation Solution
A resilience measurement module that constructs a service-level to component-level mapping structure, assigns criticality indices, identifies single points of failure, calculates vulnerability scores, determines recoverability and impact analysis, and constructs a risk charter with red, amber, and green status to evaluate the resilience of multi-site, multi-vendor hybrid IT infrastructure environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems measure resilience only at application or system level considering availability and downtime, then the measurement process is simple, but the measurement precision is insufficient to capture enterprise considerations
Solution Approach 1:
The patent segments the resilience measurement into multiple hierarchical levels: enterprise level, system level, and component level. Each level has its own metrics and mapping structures, allowing comprehensive measurement without overwhelming complexity at any single level. The service level to component level mapping structure breaks down the measurement into manageable segments.
Solution Approach 2:
The patent adds multiple dimensions to resilience measurement beyond just availability and downtime. It incorporates monitoring status, component recoverability, backups, and severity of impact as additional measurement dimensions. This multi-dimensional approach significantly improves measurement precision while systematically managing complexity through structured frameworks.
2Reliability
If the system considers multiple factors like monitoring status, recoverability, and backups, then the resilience evaluation becomes comprehensive, but the calculation complexity increases
Solution Approach 1:
The patent divides the comprehensive resilience evaluation into distinct calculable components: availability vulnerability score, recoverability factor, performance vulnerability score, backup vulnerability score, and impact analysis score. Each component is calculated separately using specific formulas and then aggregated, making the complex evaluation manageable and systematic.
Solution Approach 2:
The system incorporates feedback mechanisms where vulnerability scores and factors are continuously calculated based on current system state, monitoring data, and component performance. This feedback loop allows the system to dynamically adjust resilience evaluations while maintaining computational tractability through standardized calculation procedures.
3Measurement precision
If the system identifies critical components and single points of failure, then the resilience measurement becomes more accurate, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary identification of critical components and single points of failure by establishing service level to component level mapping structures and business component to technical component mapping structures in advance. This preliminary action allows the system to quickly identify critical elements during resilience assessment without performing exhaustive analysis each time, thus reducing analysis time while maintaining accuracy.
4Loss of information
If the system constructs detailed mapping structures and calculates multiple vulnerability scores, then the resilience visualization becomes comprehensive, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple vulnerability scores (availability, performance, backup) and factors (criticality indices, recoverability) into a unified resilience measurement framework. The risk charter consolidates all this information into a single comprehensive visualization with red, amber, and green status indicators, reducing data processing complexity by integrating disparate data streams into a cohesive output.
Data Source
AI summary
Automatic computation of a resilience of a hybrid IT infrastructure environment based on a variety of factors including resilience of individual architectural components in combination with the business criticality of each of those components.