Application Priority System for Computing Resource Allocation

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Solution Overview

Problem

Existing computing application resource management technologies are inefficient and lack insights into application priority, leading to inadequate resource allocation and delayed task completion, particularly due to the scarcity of specialized resources and users.

Innovation Solution

A machine-learning-derived application prioritization system using a feedback-based model with linear regression to determine relative priorities of computing applications, facilitating efficient resource allocation and user-specific prioritization, thereby optimizing infrastructure utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If specialized resources are distributed amongst multiple computing applications, then resource utilization increases, but task completion reliability decreases

Engineering Contradiction:

Improveresource utilizationVSAvoidtask completion reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback mechanisms by continuously monitoring application performance metrics, resource consumption patterns, and task completion status. This feedback loop enables dynamic adjustment of resource allocation to maintain both high utilization and reliable task completion. The feedback-driven approach allows the system to respond to changing conditions and prioritize critical tasks automatically.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes allocation parameters dynamically based on application priority levels, task criticality, and resource availability. By adjusting parameters such as resource quotas, scheduling weights, and allocation thresholds in real-time, the system optimizes the balance between resource utilization efficiency and task completion reliability without manual intervention.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual resource allocation is used for computing applications, then resource control precision increases, but allocation efficiency decreases

Engineering Contradiction:

Improveresource control precisionVSAvoidallocation efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system enables self-service automated resource allocation that maintains precise control without manual intervention. The intelligent agent autonomously monitors application needs, evaluates priority levels, and allocates resources accordingly, achieving both high precision and efficiency. The system self-adjusts based on observed patterns and performance metrics.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical resource allocation processes with automated intelligent algorithms. The mechanical process of human decision-making and manual configuration is substituted with computational models that analyze multiple parameters simultaneously, resulting in both precise control and rapid allocation efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If specialized users are tasked with advising computing application teams, then application quality improves, but task completion speed decreases

Engineering Contradiction:

Improveapplication qualityVSAvoidtask completion speed

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system introduces an intelligent intermediary layer that automatically translates application requirements into optimized resource allocation decisions. This intermediary eliminates the need for specialized users to manually advise each application team, yet maintains high application quality through automated expert-level analysis and decision-making.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary analysis and preparation of resource allocation strategies in advance, so that when applications need resources, the optimal allocation is already determined. This preliminary action eliminates delays that would occur if specialized users needed to analyze and advise on each request in real-time.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12026554B2Query-response system for identifying application priority

Publication Date: 2024.07.02 BANK OF AMERICA CORP

AI summary

A resource management system receives a set of application priorities. The resource management system determines, based at least in part on the received set of application priorities, a resource allocation corresponding to a proposed distribution of the computing applications and the users amongst the computing devices of a computing infrastructure. The resource management system determines, using the resource allocation, a recommended device configuration for each of the computing devices. The resource management system automatically implements the determined resource allocation using the device configuration determined for each of the computing devices.