Virtualized Capacity Management via Probabilistic Forecasting

The patent merges resource management at the workload level rather than individual virtual machine level. The capacity management service aggregates resource consumption data across multiple virtual machines belonging to the same workload, enabling centralized entitlement and limit configuration that simplifies operations while maintaining precise resource control through workload-level metrics and probabilistic forecasting.

The patent introduces a capacity management service as an intermediary layer between the virtualization infrastructure and workloads. This service acts as a mediator that collects capacity consumption data, performs probabilistic forecasting, and manages entitlements and limits, thereby reducing direct management complexity while improving resource control precision through automated decision-making.

The patent implements dynamic resource allocation by using probabilistic forecasting to predict future capacity consumption at different confidence levels. Instead of static peak-demand-based entitlements, the system dynamically adjusts resource allocation based on predicted demand patterns, allowing entitlements to be set at appropriate percentiles (e.g., 90th percentile) that balance availability and efficiency based on actual workload characteristics.

The patent changes the parameter basis for resource entitlement from fixed peak demand values to probabilistic forecast percentiles. By calculating capacity consumption at different confidence levels (e.g., 50th, 90th, 95th percentiles), the system enables flexible parameter adjustment that optimizes both resource availability and utilization efficiency based on organizational risk tolerance and service level requirements.

The patent implements feedback mechanisms by continuously monitoring actual capacity consumption against probabilistic forecasts and entitlement limits. The capacity management service collects consumption data, compares it with predicted values at different confidence levels, and adjusts entitlements and limits accordingly, enabling adaptive resource management that prevents both starvation and inefficiency through data-driven decision-making.

The patent performs preliminary resource allocation based on probabilistic forecasting before workloads actually consume resources. By predicting future capacity consumption at various confidence levels and pre-configuring entitlements and limits accordingly, the system proactively prevents resource starvation while avoiding overly conservative allocations that would reduce operational efficiency.