VM Configuration via Source Code Analysis

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Solution Overview

Problem

Information processing systems face inefficiencies in configuring virtual machines (VMs) due to users often requesting initial resource configurations that are either too excessive or insufficient, leading to underutilization of resources, particularly when resource addition is limited, such as during semiconductor chip shortages.

Innovation Solution

An automated method that parses application source code to determine features and uses a machine learning model trained on historical usage data to optimize VM configurations, ensuring accurate and efficient resource allocation based on application needs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If users manually configure VM resources, then flexibility in resource allocation is maintained, but resource underutilization occurs due to excessive or insufficient initial configurations

Engineering Contradiction:

Improveresource allocation flexibilityVSAvoidresource underutilization

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system performs self-service by automatically analyzing application source code and using machine learning models to determine optimal VM configurations without manual user input, thereby eliminating resource underutilization while maintaining allocation flexibility

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of application source code and historical usage data before VM deployment to pre-determine optimal resource configurations, preventing both excessive and insufficient resource allocation from the outset

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If automated source code analysis is implemented, then VM configuration accuracy is improved, but system complexity increases

Engineering Contradiction:

ImproveVM configuration accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces an intermediary machine learning model that bridges source code analysis and VM configuration determination, automatically extracting features and mapping them to optimal configurations without requiring complex manual intervention

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by transforming source code into extractable features and then into configuration parameters through the machine learning model, simplifying the overall process despite the added automation layer

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240020142A1Determining virtual machine configuration based on application source code

Publication Date: 2024.01.18 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for determining a virtual machine configuration based on application source code are provided herein. An example computer-implemented method includes parsing source code of an application to determine one or more features of the application; providing the one or more features to at least one machine learning model, wherein the machine learning model is trained based at least in part on historical usage data associated with one or more virtual machines configured for one or more other applications; obtaining, from the at least one machine learning model, one of a plurality of virtual machine configurations for the application; and initiating a configuration of at least one virtual machine for the application based at least in part on the virtual machine configuration obtained from the at least one machine learning model.