ML Workspace Deployment Prediction Engine

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Solution Overview

Problem

Current technologies face challenges in efficiently deploying and managing machine learning (ML) workspaces across multiple datacenters and regions, leading to issues such as suboptimal resource utilization, performance degradation, and increased operational costs.

Innovation Solution

The proposed solution involves creating an AI/ML resource knowledge base and using Deep Neural Networks (DNNs) alongside reinforcement learning techniques to predict the optimal size of ML workspaces and identify suitable environments for deployment, ensuring efficient resource allocation and management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If AI/ML workspaces are deployed across multiple datacenters and regions using Kubernetes, then scalability and flexibility are improved, but deployment complexity and operational difficulty increase

Engineering Contradiction:

Improvedeployment flexibilityVSAvoiddeployment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements automated workspace placement decisions where the placement engine autonomously selects optimal datacenters and hosts based on predicted workspace characteristics, eliminating the need for manual architectural planning and configuration by software architects

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses historical workspace data and predicted characteristics to feed back into the placement decision process, continuously improving accuracy of workspace size predictions and placement optimizations through iterative learning from actual workspace performance

Inventive Principle:

Principle #23Feedback

2Productivity

If workspaces are manually scheduled and placed across datacenters, then deployment control is maintained, but resource utilization efficiency deteriorates

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoiddeployment automation level

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system replaces manual mechanical scheduling processes with an automated placement engine that uses machine learning models to predict workspace characteristics and automatically makes placement decisions, substituting human expertise with algorithmic decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the approach from static manual scheduling to dynamic automated placement based on predicted workspace parameters such as size, resource requirements, and optimal location, allowing real-time optimization of resource allocation

Inventive Principle:

Principle #35Parameter changes

3Reliability

If incorrect scheduling and placement occur, then deployment speed is maintained, but performance and network latency deteriorate

Engineering Contradiction:

Improveworkspace performanceVSAvoiddeployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary prediction of workspace characteristics and optimal placement decisions before actual deployment occurs, using historical data and machine learning models to pre-determine best practices for scheduling and resource allocation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system copies successful placement patterns from historical workspace data and uses these patterns as templates for new deployments, replicating proven strategies that have worked in the past to ensure optimal performance without time-consuming analysis

Inventive Principle:

Principle #26Copying

4Measurement precision

If automated placement decisions are made without predictive models, then deployment simplicity is maintained, but resource allocation accuracy deteriorates

Engineering Contradiction:

Improveworkspace size prediction accuracyVSAvoidpredictive modeling complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system copies and leverages historical workspace data and successful placement patterns to train predictive models, using past performance information to accurately predict future workspace requirements without requiring complex real-time analysis

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary training of predictive models using historical data before actual deployment decisions are made, pre-calculating placement strategies based on past workspace characteristics and outcomes to improve prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250045119A1Optimized deployment of cloud native workspaces and jobs across multiple infrastructures for high scalability and performance

Publication Date: 2025.02.06 DELL PROD LP

AI summary

One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model, predicting, by the workspace size predicting engine, a size of a workspace that corresponds to the workspace provisioning request, receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, and predicting, by the datacenter host prediction engine, a datacenter and/or host that is able to support requirements of the workspace.