Multi-API VDI Orchestration for Low Per-User Resource Utilization

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Solution Overview

Problem

Existing VDI environments face inefficiencies in managing API calls to minimize overall resource utilization, particularly in multi-vendor environments, leading to suboptimal resource allocation and user experience.

Innovation Solution

An orchestrator system intelligently combines vendor-provided APIs to optimize resource utilization by training models on user and environment data, selecting the combination that minimizes per-user average resource usage, and deploying and refining the VDI environment based on machine learning and artificial intelligence.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple VDI environments are assembled using multiple API calls, then the versatility and adaptability of the system is improved, but the device complexity and difficulty of managing API combinations increases

Engineering Contradiction:

ImproveVDI environment configuration optionsVSAvoidAPI combination management

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system employs machine learning models that automatically analyze resource utilization patterns and self-select optimal API combinations without manual intervention. The orchestrator autonomously manages the complexity of assembling multiple VDI environments from different vendors, allowing the system to serve itself in optimizing configurations based on observed performance data

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the state of API combination management from static manual configuration to dynamic automated selection. By using machine learning to continuously evaluate and adjust API parameter combinations based on resource utilization metrics, the system transforms complex manual API assembly into an adaptive automated process that optimizes VDI environment configurations

Inventive Principle:

Principle #35Parameter changes

2Loss of energy

If resource utilization is minimized through optimized API combinations, then the loss of energy is reduced, but the difficulty of detecting and measuring optimal configurations increases

Engineering Contradiction:

Improveoverall resource utilizationVSAvoidoptimal API combination identification

Core Design Contradiction:

Loss of energyVSDifficulty of detecting and measuring

Solution Approach 1:

The system implements continuous feedback loops where machine learning models monitor resource utilization metrics from VDI environments and use this feedback to iteratively improve API combination selections. The orchestrator collects performance data, analyzes it through ML models, and adjusts API configurations accordingly, creating a closed-loop system that continuously reduces energy consumption while automatically identifying optimal settings

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual trial-and-error methods for identifying optimal API combinations with machine learning-based automated detection. Instead of mechanically testing configurations, the system uses ML algorithms to predict and identify optimal API combinations based on learned patterns from historical performance data, significantly reducing the difficulty of detecting optimal configurations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If manual assembly of API calls is used to manage VDI environments, then the device complexity is reduced, but the productivity and efficiency of resource management deteriorates

Engineering Contradiction:

Improvesystem architectureVSAvoidresource management efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent introduces an orchestrator as an intermediary layer between manual configuration inputs and the actual VDI environment deployment. This orchestrator component automates the assembly of API calls and the selection of optimal configurations, bridging the gap between simple user inputs and complex resource management tasks, thereby improving productivity without significantly increasing perceived system complexity for users

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12632297B2Aggregate resource-based flex on demand in a multi-API virtual desktop infrastructure (VDI) environment

Publication Date: 2026.05.19 DELL PROD LP

AI summary

A method for managing VDI environments includes: obtaining a first resource related parameter (FRRP) for a first resource (FR) and a second resource related parameter (SRRP) for a second resource (SR) from a database; assembling a set of API combinations to generate a set of VDI environments; testing, for a per user average resource utilization value (PUARUV) of the FR and SR, each VDI environment across a range of users based on the FRRP and SRRP; providing the range of users and set of VDI environments to an analyzer, in which the analyzer is instructed to generate a model that ranks the set of VDI environments to obtain a VDI environment that provides the lowest PUARUV; generating a trained model by training the model using the range of users, set of VDI environments, PUARUVs, FRRP, and SRRP; and initiating notification of an administrator about the trained model.