Automated Cloud-Native Application Deployment Assessment
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Solution Overview
Problem
The deployment of cloud-native applications is labor-intensive, inaccurate, and error-prone due to the complexity of determining suitable cloud providers, scaling factors, and resource configurations for business-critical applications.
Innovation Solution
An apparatus and method that generate an application deployment specification based on workload profiles and deployment parameters, identify the type of orchestrators, and deploy applications through an API, collecting operational data for assessment, thereby automating the deployment process and providing statistics for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual deployment processes are used for cloud-native applications, then flexibility in customization is maintained, but labor intensity and error rates increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a deployment specification composer, API server, and dry run manager that mediates between manual deployment processes and orchestration systems. This intermediary automates the generation of deployment specifications from application specifications and workload profiles, processes them according to orchestrator types, and enables dry-run assessments before actual deployment, thereby reducing labor intensity while managing complexity through structured automation layers
Solution Approach 2:
The deployment process is segmented into distinct modular components: application specification generation, deployment specification composition, orchestrator type identification, specification processing, dry-run execution, and actual deployment. Each segment is handled by specialized modules (composer, API server, dry run manager), allowing automated processing of individual deployment aspects while maintaining overall process flexibility and reducing errors through systematic division of labor
2Productivity
If automated deployment tools are implemented, then labor intensity is reduced, but accuracy and precision of deployment parameters may deteriorate
Solution Approach 1:
The system performs preliminary actions by generating deployment specifications from application specifications and workload profiles before actual deployment. The dry-run manager executes dry-run assessments that simulate deployment outcomes and collect operational data, allowing validation and adjustment of deployment parameters before committing resources, thereby ensuring accuracy while maintaining automated efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the dry run manager collects operational data from monitoring agents during dry-run assessments, and this data is used to validate deployment specifications and adjust parameters. The system provides feedback on deployment outcomes, resource utilization, and performance metrics, enabling continuous refinement of deployment accuracy while maintaining automated processing efficiency
3Reliability
If comprehensive deployment assessments are performed, then deployment quality is improved, but time consumption and resource usage increase
Solution Approach 1:
The system performs partial assessment actions through dry-run executions that simulate deployment without full resource commitment. The dry run manager conducts selective assessments of deployment specifications, evaluating critical aspects such as resource requirements and orchestration compatibility without executing complete deployment cycles, thereby maintaining deployment quality while reducing time consumption through targeted rather than exhaustive assessment
Data Source
AI summary
An apparatus and associated method are provided for application deployment assessment. In use, a plurality of deployment parameters associated with one or more applications, and a workload profile are received. Further, an application deployment specification is generated, based on the workload profile and the deployment parameters. Still yet, a type of one or more orchestrators on one or more systems is identified. The application deployment specification is processed, based on the identified type of the one or more orchestrators on the one or more systems. Further, the one or more processors execute the instructions to deploy, via an application program interface (API), the one or more applications to the one or more orchestrators on at least one of the one or more systems, and at least one workload generator to at least one of the one or more systems, utilizing the processed application deployment specification. Operational data is collected from one or more monitoring agents on the one or more systems. One or more statistics are generated for assessing the deployment of the one or more applications, based on the operational data.