Storage Configuration Templates via ML Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional storage system management approaches rely on manual input and are prone to errors, leading to improper and inefficient configurations that negatively impact storage performance and capacity, particularly when trying to determine optimal configurations for specific use cases across different vertical sectors.
Innovation Solution
The use of machine learning techniques to segment storage systems based on vertical sectors and size parameters, identify vertical sector-specific applications, cluster storage systems for optimal configuration, and generate automated configuration templates to improve storage system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual input and human insights are used for storage system configuration, then ease of operation is maintained, but reliability and manufacturing precision deteriorate due to errors and improper configurations
Solution Approach 1:
The system performs self-service by automatically segmenting storage systems into vertical sectors, identifying sector-specific applications, clustering similar configurations, and generating configuration templates without human intervention. This automated self-configuration process eliminates manual errors while maintaining system complexity through structured algorithms.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an automated information processing system that uses machine learning algorithms for clustering and template generation. This substitution transforms the manual human-operated system into an automated computational system, improving reliability while managing complexity through algorithmic processes.
2Productivity
If manual configuration approaches are used, then device complexity is low, but productivity deteriorates due to time-consuming and error-prone manual processes
Solution Approach 1:
The system performs preliminary actions by pre-segmenting storage systems into vertical sectors and pre-identifying sector-specific applications before configuration is needed. This advance preparation enables faster configuration generation and improves productivity by eliminating the need for manual analysis during the configuration process.
Solution Approach 2:
The patent creates configuration templates that serve as reusable copies of optimal configurations. Once a configuration is generated and validated for a particular vertical sector, it is copied and applied to similar systems, dramatically improving productivity by eliminating redundant manual configuration work while managing complexity through template management.
3Adaptability or versatility
If storage systems are configured without vertical sector segmentation, then device complexity is reduced, but adaptability deteriorates as systems cannot be optimized for specific use cases
Solution Approach 1:
The patent applies segmentation by dividing storage systems into distinct vertical sectors (e.g., finance, healthcare, retail) based on enterprise characteristics and application types. This segmentation enables tailored configuration optimization for each sector while managing complexity through structured categorization and sector-specific configuration templates.
Solution Approach 2:
The system implements local quality by providing customized configuration optimizations specific to each vertical sector and its unique requirements. Instead of a one-size-fits-all approach, each sector receives locally optimized configurations tailored to its specific use cases, improving adaptability while managing complexity through sector-specific parameter sets.
4Reliability
If automated machine learning techniques are implemented, then reliability and productivity improve, but device complexity increases due to advanced processing requirements
Solution Approach 1:
The patent introduces an intermediary layer of configuration templates that mediate between the complex machine learning processing and the final storage system configuration. The machine learning models generate templates that serve as intermediaries, simplifying the interface between complex automated processing and the actual storage systems being configured, thereby managing processing complexity while maintaining reliability.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for determining storage system configuration recommendations based on vertical sectors and size parameters using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple storage systems; segmenting, into one or more segments, the multiple storage systems based on one or more vertical sectors and one or more size parameters of an enterprise associated with each storage system; identifying, within each of the segments, each storage system running one or more vertical sector-specific applications; clustering, within each of the segments, the storage systems running one or more vertical sector-specific applications based on configuration information using at least one machine learning technique; generating one or more storage system configuration templates based on the clustering; and performing one or more automated actions based on the one or more generated storage system configuration templates.