Dynamic Computing Resource Availability Rating via Probability Models

The patent introduces an intermediary system that sits between the virtualization layer and the physical infrastructure, translating and correlating data from multiple sources including virtual machine metrics, physical hardware status, and application performance. This intermediary layer reconstructs the obscured health information by aggregating data from various levels of the stack, making it visible and actionable despite the virtualization abstraction.

The system adds a new dimension of observation by creating a multi-layered view that correlates virtual, physical, and application-level metrics simultaneously. Instead of viewing infrastructure health from a single perspective, the system creates a holistic model that spans multiple dimensions including virtual machine status, physical hardware health, network conditions, and application performance, enabling detection that would be impossible from any single layer alone.

The patent implements preliminary action by automatically discovering and mapping all application dependencies and infrastructure relationships before outages occur. The system continuously builds and updates a dependency graph that captures multi-layer relationships between applications, virtual machines, physical hardware, and external services. This pre-established knowledge base enables rapid impact analysis when outages occur, eliminating the need to manually trace complex dependency chains during crisis situations.

The system replaces manual dependency analysis and troubleshooting processes with an automated computational approach. Instead of requiring operators to manually trace through complex multi-layer dependencies during outages, the system uses automated algorithms to instantly calculate and present the full scope of impacted applications and services, substituting mechanical human analysis with automated computational reasoning.

The patent applies the extraction principle by selectively pulling out and focusing on the most critical metrics and dependencies that actually impact service availability. Instead of monitoring every possible parameter uniformly, the system identifies and extracts the key performance indicators and dependency relationships that are most relevant to outage detection and impact analysis, reducing data volume while maintaining detection effectiveness.

The system implements local quality by applying different monitoring intensities and granularities to different parts of the infrastructure based on their criticality and risk profiles. Critical components and applications receive more detailed monitoring and analysis, while less critical elements receive lighter monitoring. This differentiated approach optimizes resource utilization and reduces overall system complexity while maintaining high reliability for critical services.