Intent-Based Computing Resource Allocation via Optimization

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Solution Overview

Problem

Traditional resource planning in cloud computing and distributed systems is inefficient and prone to errors due to manual calculations and labor-intensive processes, leading to slow adaptation to changes and non-optimal solutions.

Innovation Solution

A method for allocating computing resources based on user intent data, using linear or mixed integer programming to optimize resource allocation, considering priority values, budget constraints, and available resources, which generates a resource allocation problem to meet specific objectives and adapt to changes automatically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual resource planning is used, then resource allocation can be performed, but the process is slow and requires intensive manual labor

Engineering Contradiction:

Improveresource allocation speedVSAvoidmanual labor requirement

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system automatically performs resource allocation by receiving intent data from users and autonomously generating resource allocation plans using optimization algorithms, eliminating the need for manual intervention in the allocation process while maintaining user control through intent specifications

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical resource planning with an automated computational system that uses linear programming and mixed-integer programming algorithms to automatically generate optimal resource allocation plans, substituting human calculation and decision-making with machine-based optimization

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual resource planning is used, then resource allocation can be performed, but human errors and inefficiencies occur

Engineering Contradiction:

Improveallocation accuracyVSAvoidmanual process involvement

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system replaces manual resource planning processes with automated computational algorithms that eliminate human errors in calculation and decision-making, using mathematical optimization models to ensure accurate and consistent resource allocation outcomes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the optimization algorithms continuously evaluate resource allocation plans against constraints and objectives, automatically adjusting allocations to achieve optimal results while ensuring reliability through iterative refinement

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If traditional resource planning is used, then resource allocation can be performed, but the system is slow to adapt to changes

Engineering Contradiction:

Improveadaptation speed to changesVSAvoidplanning time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system dynamically adapts to changes by receiving updated intent data and constraints, automatically re-running optimization algorithms to generate new resource allocation plans that reflect current conditions, enabling rapid adaptation without manual re-planning

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system autonomously handles adaptation to changes by automatically detecting updated requirements and regenerating allocation plans without human intervention, maintaining up-to-date resource allocations that respond quickly to changing conditions

Inventive Principle:

Principle #25Self-service

4Manufacturing precision

If manual resource planning is used, then resource allocation can be performed, but non-optimal solutions result from human inefficiencies

Engineering Contradiction:

Improveallocation optimalityVSAvoidcomputational model complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system replaces manual judgment and heuristic decision-making with sophisticated mathematical optimization algorithms including linear programming and mixed-integer programming, which systematically explore the solution space to guarantee optimal resource allocation outcomes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms complex resource allocation problems into structured optimization models with defined parameters, constraints, and objective functions, allowing computational algorithms to efficiently navigate complex solution spaces and identify optimal allocations that manual processes cannot achieve

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12169738B2Allocating computing resources based on user intent

Publication Date: 2024.12.17 GOOGLE LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for allocating computing resources. In one aspect, a method includes receiving intent data specifying one or more computing services to be hosted by a computing network, requested characteristics of computing resources for use in hosting the computing service, and a priority value for each requested characteristic. A budget constraint is identified for each computing service. Available resources data is identified that specifies a set of available computing resources. A resource allocation problem for allocating computing resources for the one or more computing resources is generated based on the intent data, each budget constraint, and the available resources data. At least a portion of the set of computing resources is allocated for the one or more computing services based on results of evaluating the resource allocation problem to meet a particular resource allocation objective.