Automated Storage Provisioning for Cloud Infrastructure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual workflows for storage updates in large-scale cloud computing environments are labor-intensive and costly, making flexible storage infrastructure management challenging.
Innovation Solution
Automated storage provisioning system that receives planning input for storage area network volume controllers, collects configuration data, filters candidate components, and analyzes performance data to accurately identify suitable components for handling potential loads, enabling automated and efficient storage planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual workflow is used for storage updates, then storage infrastructure can be managed with simple processes, but labor intensity and cost increase significantly
Solution Approach 1:
The system enables automated self-service storage provisioning where the storage management system automatically analyzes workload requirements, selects appropriate storage components, and executes provisioning without manual intervention. The automated workflow includes collecting configuration data, filtering candidate components, analyzing performance metrics, and executing storage updates autonomously.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational processes. Instead of human operators manually creating tickets and executing changes, the system uses automated scripts, algorithms, and integrated APIs to perform storage provisioning, substitution, and updates based on analyzed workload requirements.
2Device complexity
If manual workflow is used for storage updates, then operational complexity remains low, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting configuration data from storage components, pre-defining filtering criteria for candidate selection, and pre-establishing performance thresholds. This preparation enables rapid automated decision-making during actual storage provisioning operations, reducing execution time while maintaining manageable system complexity through structured data collection and analysis frameworks.
3Measurement precision
If automated storage provisioning is implemented, then storage provisioning accuracy improves, but system complexity increases
Solution Approach 1:
The automated provisioning system incorporates feedback mechanisms that continuously monitor storage component performance, workload patterns, and provisioning outcomes. This feedback is used to refine selection criteria, adjust filtering parameters, and optimize future provisioning decisions, improving accuracy over time while managing system complexity through iterative improvement and learned patterns.
Data Source
AI summary
The present invention provides an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). The present invention will receive planning input for a set of storage area network volume controllers (SVCs), the planning input indicating a potential load on the SVCs and its associated components. Configuration data for a set of storage components (i.e., the set of SVCs, a set of managed disk (Mdisk) groups associated with the set of SVCs, and a set of backend storage systems) will also be collected. Based on this configuration data, the set of storage components will be filtered to identify candidate storage components capable of addressing the potential load. Then, performance data for the candidate storage components will be analyzed to identify an SVC and an Mdisk group to address the potential load.