Cloud Resource Capacity Prediction With Parallel Microservices

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Solution Overview

Problem

Existing systems face inefficiencies, inaccuracies, and difficulties in timely and adaptive capacity control of resources in large-scale cloud infrastructures due to manual spreadsheet building, low automation, and complex data management, leading to performance issues and inflexibility in adapting to changing capacity needs.

Innovation Solution

A system utilizing parallel microservices and machine-readable instructions for resource capacity prediction and control, including data collection, observation, and preemptive actions, facilitated by a graphical user interface for orchestration and visualization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual spreadsheet building is used for capacity control, then ease of operation is improved, but productivity and accuracy deteriorate due to cumbersome processes and calculation errors

Engineering Contradiction:

Improveease of operationVSAvoidproductivity

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces manual spreadsheet-based capacity control with an automated cloud-based system that uses machine learning models, parallel processing, and integrated APIs to automatically collect data, generate predictions, and execute capacity control decisions, eliminating manual calculations and spreadsheet operations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service capacity control by automatically collecting resource metrics from cloud infrastructure components, generating capacity predictions using trained models, and executing control actions without requiring manual intervention or architectural expertise, allowing the system to manage itself

Inventive Principle:

Principle #25Self-service

2Device complexity

If manual capacity control processes are used, then device complexity is reduced, but measurement precision and reliability deteriorate due to errors and lack of automation

Engineering Contradiction:

Improvedevice complexityVSAvoidmeasurement precision

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces manual measurement and calculation processes with automated machine learning models that continuously collect and analyze resource metrics from cloud infrastructure, providing precise and reliable capacity predictions through computational algorithms rather than manual calculations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If comprehensive data collection is performed, then measurement precision is improved, but loss of time increases due to the time required to collect and process large amounts of data

Engineering Contradiction:

Improvemeasurement precisionVSAvoidloss of time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the data collection process into parallel microservices that simultaneously collect different types of resource metrics (CPU, memory, storage, network) from multiple cloud infrastructure components, dividing the comprehensive data collection task into concurrent operations that reduce total processing time

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements continuous data collection through automated APIs that continuously monitor resource metrics without interruption, allowing the machine learning models to receive up-to-date information and generate real-time capacity predictions without idle time between data collection cycles

Inventive Principle:

Principle #20Continuity of useful action

4Adaptability or versatility

If adaptive capacity control is implemented, then adaptability is improved, but device complexity increases due to the need for multiple prediction rules and preemptive actions

Engineering Contradiction:

ImproveadaptabilityVSAvoiddevice complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic capacity control by using machine learning models that automatically adapt to changing cloud infrastructure conditions, resource usage patterns, and capacity predictions, allowing the system to dynamically adjust capacity allocations without manual reconfiguration or complex rule-based logic

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260030065A1Systems and methods to facilitate adaptive resource capacity prediction and control using cloud infrastructures

Publication Date: 2026.01.29 THE HUNTINGTON NAT BANK

AI summary

Systems, methods, and non-transitory, machine-readable media may facilitate adaptive resource capacity prediction and control using cloud infrastructures. Specifications of resource allocations for resources provided by a cloud infrastructure system may be collected. Execution of a series of sets of parallel microservices may be caused. Each set may be a function of a particular type of resource data and may facilitate obtaining resource metrics data corresponding to the particular type. The series of sets may facilitate obtaining resource metrics data mapped to the resources provided by the cloud infrastructure system. Prediction rules may be selected as a function of particular resource metrics. The selected prediction rules may be used to predict resource capacities for a subset of the resources as a function of the particular resource metrics and generate resource capacity predictions. Preemptive actions with respect to incidents identified based on the resource capacity predictions may be facilitated.