Storage Classification Engine Tracing Mount Points
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Solution Overview
Problem
Data centers face challenges in rapidly and accurately classifying storage infrastructure due to complexities arising from rapid technological advancements, growing demand, and the need for continuous updates, which leads to bottlenecks and inefficiencies in managing resources and dependencies within the IT environment.
Innovation Solution
A classification system that utilizes a processor and memory to discover and classify files across computer storage systems by tracing them from file system mount points through objects to disk objects, associating them with classification categories and storing the information in data structures, thereby automating the process and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used to characterize storage infrastructure, then flexibility in handling complex dependencies is maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs automated self-classification of storage infrastructure by traversing dependency graphs and automatically resolving storage types (NAS, SAN, RAID, direct-attached) without requiring manual intervention. The classifier engine autonomously processes storage objects, resolves their types based on discovered dependencies, and populates classification data structures, enabling the system to serve its own classification needs efficiently and accurately.
2Measurement precision
If detailed documentation of storage infrastructure is maintained, then accuracy of classification is improved, but the complexity of managing documentation increases
Solution Approach 1:
The discovery engine performs preliminary traversal and documentation of the storage infrastructure dependency graph before classification occurs. By pre-discovering and recording all storage objects, their relationships, and dependencies in structured data formats, the system prepares the necessary information in advance, making the subsequent classification process simpler and more accurate without requiring complex real-time documentation management.
Solution Approach 2:
The patent introduces intermediate data structures (storage object representations, dependency graphs, and classification data structures) that mediate between the raw storage infrastructure and the classification process. These intermediaries organize and standardize storage information, reducing the complexity of managing detailed documentation while maintaining high classification accuracy through structured data representation.
3Productivity
If storage infrastructure is rapidly modernized, then productivity is improved, but the risk of errors and loss of information increases
Solution Approach 1:
The system performs preliminary discovery and classification of storage infrastructure before modernization transformations begin. By pre-traversing dependency graphs, identifying all storage objects, and resolving their types in advance, the system creates a complete inventory and classification record that serves as a reliable reference during rapid modernization, preventing errors and information loss while maintaining high productivity.
Solution Approach 2:
The classification system provides continuous feedback about storage object types, dependencies, and relationships during the modernization process. This feedback mechanism ensures that transformations are based on accurate, up-to-date classification information, reducing errors and maintaining reliability even as modernization progresses rapidly through automated workflows.
Data Source
AI summary
A classification system executing on one or more computer systems includes a processor and a memory coupled to the processor. The memory includes a discovery engine configured to navigate through non-volatile memory storage to discover an identity and location of one or more files in one or more computer storage systems by tracing the one or more files from file system mount points through file system objects and to disk objects. A classifier is configured to classify the one or more the files into a classification category. The one or more files are associated with the classification category and stored in at least one data structure. Methods are also provided.