Deployment Metrics for Software Resource Estimation

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Solution Overview

Problem

In distributed computing environments, accurately determining the computing resource requirements for software programs is challenging due to their variable nature, making it difficult to preconfigure environments and assess their suitability for deployment.

Innovation Solution

A system generates deployment metrics based on a deployment specification, estimating resource consumption and providing actionable insights to configure environments and make informed deployment decisions, using a text file with declarative format and leveraging automation frameworks like Kubernetes for reconciliation processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If deployment specifications are created for software programs in distributed computing environments, then the target deployment state can be monitored and divergences resolved automatically, but it is difficult to accurately determine computing resource requirements due to the variable nature of software programs

Engineering Contradiction:

Improveautomation of deployment monitoring and reconciliationVSAvoidaccuracy of resource consumption estimation

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by generating resource consumption estimates and deployment metrics before actual deployment occurs. The processor generates estimates of computing resource requirements based on the deployment specification and software program characteristics, allowing users to assess deployment suitability in advance without needing to know precise resource requirements beforehand.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary layer that generates deployment metrics and resource consumption estimates as intermediate data between the deployment specification and the actual deployment decision. This intermediary processing layer translates deployment specifications into actionable insights about resource requirements and deployment desirability.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If deployment specifications include detailed deployment parameters, then the target deployment state can be precisely defined, but it becomes challenging to assess the desirability of different deployment environments without knowing actual resource consumption

Engineering Contradiction:

Improveprecision of target deployment state definitionVSAvoidease of deployment environment assessment

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system enables self-service by automatically generating resource consumption estimates and deployment metrics without requiring manual intervention or prior knowledge of actual resource usage. The processor autonomously analyzes the deployment specification and software program to produce deployment metrics that guide environment selection and deployment decisions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual assessment mechanisms with automated computational processes. Instead of requiring users to manually evaluate deployment environments based on uncertain resource requirements, the system uses automated processing to generate accurate resource consumption estimates and deployment metrics, substituting mechanical evaluation with computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of information

If the system generates deployment metrics before deployment, then users can make informed decisions about resource allocation and environment selection, but additional processing time and computational resources are required

Engineering Contradiction:

Improveinformation completeness for deployment decisionsVSAvoidtime for generating deployment metrics

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs the beneficial action of generating deployment metrics in advance, before deployment decisions need to be made. By computing resource consumption estimates and deployment metrics preliminarily, the system ensures that complete information is available for informed decision-making without delaying the actual deployment process.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11914980B2Managing deployments of software programs to distributed computing environments

Publication Date: 2024.02.27 RED HAT LLC

AI summary

Deployments of software programs to distributed computing environments can be managed according to some aspects described herein. In one example, a system can receive a deployment specification having deployment parameters that define a target deployment state for a software program deployable to a target computing environment. The system can generate, based on a mapping of the deployment parameters to resource consumption values, a resource consumption estimate associated with the target deployment state of the software program in the target computing environment. The system can generate a deployment metric based on the resource consumption estimate and transmit the deployment metric to a client device. The deployment metric can be usable to manage deployment of the software program.