AI Agent Pre-Build Cloud Configuration for Throttling and Slack

Solution Overview

Problem

Configuring cloud services with optimal resource allocation is challenging due to the complexity of translating use cases into resource capability provisioning, leading to issues of throttling or excessive slack, which affects performance and costs.

Innovation Solution

An AI agent uses prior-existing utilization data and project metadata to create a capacity prediction model for generating a pre-build configuration that balances cost and performance, minimizing throttling and slack by leveraging hierarchical and target encoding models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If customers configure cloud resources conservatively to avoid throttling, then performance reliability is improved, but resource utilization efficiency deteriorates due to excessive slack capacity

Engineering Contradiction:

Improveperformance reliabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of historical utilization data and project metadata before resource provisioning to predict optimal capacity requirements. This advance preparation enables customers to configure resources accurately from the start, avoiding both throttling and excessive slack capacity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors resource utilization patterns and feeds this information back into the prediction model. This feedback mechanism refines future predictions and enables dynamic adjustment of resource configurations to maintain optimal balance between reliability and efficiency.

Inventive Principle:

Principle #23Feedback

2Reliability

If customers configure cloud resources generously to prevent throttling, then performance reliability is improved, but cost increases due to paying for unused capacity

Engineering Contradiction:

Improveperformance reliabilityVSAvoidresource cost

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary analysis of historical utilization data and project metadata before resource provisioning to predict optimal capacity requirements. This advance preparation enables customers to configure resources accurately from the start, avoiding both throttling and excessive slack capacity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transforms unstructured project metadata and historical data into structured predictions of optimal resource parameters. By changing the state of data from raw historical records to predictive capacity recommendations, the system enables precise resource configuration that balances cost and performance.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If customers tailor resources based on performance history, then resource configuration accuracy is improved, but time is lost during the period of collecting performance data

Engineering Contradiction:

Improveresource configuration accuracyVSAvoiddata collection period

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of historical utilization data and project metadata before resource provisioning to predict optimal capacity requirements. This advance preparation enables customers to configure resources accurately from the start, avoiding both throttling and excessive slack capacity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates predictive models that replicate the relationship between project characteristics and optimal resource configuration based on historical patterns. These models enable direct prediction of resource needs without requiring new customers to undergo the same trial-and-error data collection period.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250307107A1Ai agent for pre-build configuration of cloud services

Publication Date: 2025.10.02 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Example solutions provide an artificial intelligence (AI) agent for pre-build configuration of cloud services in order to enable the initial build of a computational resource (e.g., in a cloud service) to minimize the likelihood of excessive throttling or slack. Examples leverage prior-existing utilization data and project metadata to identify similar use cases. The utilization data includes capacity information and resource consumption information (e.g., throttling and slack) for prior-existing computational resources, and the project metadata includes information for hierarchically categorization, to identify similar resources. A pre-build configuration is generated for the customer's resource, which the customer may tune based upon the customer's preferences for a cost and performance balance point.