Predictive Resource Allocation for Shared IT Asset Pools

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Solution Overview

Problem

Existing information processing systems lack a proactive approach to dynamically adjust resource allocation based on predicted utilization, leading to reactive solutions that cause downtime and sub-optimal utilization of resources.

Innovation Solution

Implementing a resource allocation management system that utilizes machine learning models to predict resource utilization and proactively adjust resource allocation by leveraging AI/ML algorithms for dynamic scaling and remediation actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If reactive resource allocation adjustment is used, then system response to resource needs is simple and direct, but downtime occurs and resource utilization becomes sub-optimal

Engineering Contradiction:

Improvebusiness continuityVSAvoiddowntime

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by proactively predicting future resource utilization needs using machine learning models before resource exhaustion occurs. The resource allocation management system continuously analyzes monitoring data and adjusts resource allocation in advance, preventing downtime rather than reacting to it after it occurs.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where monitoring data from IT assets is collected, analyzed by machine learning models to predict future resource needs, and used to dynamically adjust resource allocation. This closed-loop feedback mechanism enables the system to adapt proactively to changing resource requirements, maintaining business continuity without interruption.

Inventive Principle:

Principle #23Feedback

2Productivity

If static resource allocation is used, then resource allocation management is simple and stable, but resource utilization efficiency decreases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidresource allocation management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system transitions from static to dynamic resource allocation by continuously adjusting resource distribution based on real-time monitoring data and machine learning predictions. Resource allocation changes dynamically in response to predicted future utilization needs, optimizing efficiency while the underlying ML models handle the complexity automatically.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The resource allocation management system performs self-service by automatically analyzing monitoring data, predicting resource needs, and adjusting allocations without manual intervention. The machine learning models autonomously manage the complexity of dynamic resource allocation, eliminating the need for complex manual management while maximizing resource utilization efficiency.

Inventive Principle:

Principle #25Self-service

3Reliability

If proactive resource allocation adjustment using machine learning is implemented, then resource utilization is optimized and downtime is reduced, but system complexity increases

Engineering Contradiction:

Improvebusiness continuityVSAvoidresource allocation management system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces machine learning models as intermediaries between monitoring data collection and resource allocation decisions. These ML models process complex patterns in monitoring data and translate them into actionable resource allocation adjustments, managing system complexity internally while delivering simplified proactive resource management outcomes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual or rule-based resource allocation mechanisms with machine learning-based automated decision-making. The ML models substitute complex mechanical or procedural management systems, automatically analyzing patterns and making optimization decisions that would otherwise require sophisticated manual management processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12561176B2Proactive adjustment of resource allocation to information technology assets based on predicted resource utilization

Publication Date: 2026.02.24 DELL PROD LP

AI summary

An apparatus comprises a processing device configured to obtain monitoring data characterizing resource utilization by information technology (IT) assets having resources assigned from a shared resource pool, to select features for use in modeling predicted resource utilization by the IT assets in future time periods, to generate predictions of resource utilization by the IT assets in each of the future time periods, and to determine whether the predicted resource utilization by a given IT asset exhibits at least a threshold difference from its current resource allocation. The processing device is further configured, response to the determination, to proactively adjust resource allocation to the given IT asset from the shared resource pool for the given future time period based at least in part on the predicted resource utilization, for the given future time period, by other ones of the IT assets having resources assigned from the shared resource pool.