Application Sizing Engine for Cloud Infrastructure Orchestration

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing cloud service and data center service provisioning systems struggle to accurately size infrastructure components based on user intent, leading to potential performance, availability, reliability, and security issues.

Innovation Solution

A method and apparatus for sizing infrastructure for an application as a service, which involves receiving performance and security information, determining the required infrastructure components and their Key Performance Indicators (KPIs) using an empirical model, and outputting this information to a service orchestration system. This process includes predicting infrastructure performance, comparing predicted and observed performance, and updating KPI weights using machine learning algorithms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If infrastructure components are sized based on traditional provisioning methods, then service delivery is achieved, but performance, availability, reliability, and security requirements cannot be accurately met

Engineering Contradiction:

Improveservice level agreement complianceVSAvoidinfrastructure sizing accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The system implements feedback loops where observed infrastructure performance is continuously compared with predicted performance from the empirical model. This feedback mechanism allows the machine learning algorithm to update weights of KPI and performance characteristics, progressively improving the accuracy of infrastructure sizing predictions to meet SLA requirements

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent transforms multiple infrastructure parameters (performance, availability, reliability, security) into homogenized space vectors that can be processed by machine learning algorithms. This parameter transformation enables more precise measurement and comparison, directly improving infrastructure sizing accuracy

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If machine learning algorithms are used to update KPI weights, then infrastructure sizing accuracy improves, but system complexity increases

Engineering Contradiction:

Improveinfrastructure sizing accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system employs self-service mechanisms where the machine learning algorithm automatically updates KPI weights based on observed versus predicted performance comparisons. This automation reduces manual intervention and operational complexity while maintaining high sizing accuracy, as the system self-optimizes without requiring complex manual configuration

Inventive Principle:

Principle #25Self-service

3Reliability

If infrastructure is oversized to meet all SLA requirements, then reliability is improved, but resource utilization efficiency decreases

Engineering Contradiction:

Improvesla complianceVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system dynamically adjusts infrastructure sizing predictions by continuously updating KPI weights based on actual performance observations. This dynamic approach allows the system to optimize resource allocation in real-time, ensuring SLA compliance while avoiding static oversizing that would waste resources. The empirical model adapts to changing conditions, maintaining reliability without excessive resource consumption

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250138977A1Machine learning based application sizing engine for intelligent infrastructure orchestration

Publication Date: 2025.05.01 RAO SHISHIR R

AI summary

This disclosure provides an apparatus, a method and a nontransitory storage medium having computer readable instructions for sizing infrastructure needed for an application as a service.