Host Device Block-Level Classification for Storage Efficiency
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Solution Overview
Problem
Storage systems often apply unnecessary data services to unstructured files due to lack of information about the files' characteristics, leading to inefficient operations.
Innovation Solution
A host device classifies unstructured data at the block level by obtaining a pointer to a page cache and an index node object, deriving file characteristics, and providing an indication to the storage system, which then determines whether to apply data services such as deduplication, encryption, or compression based on these characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the storage system applies data services to all unstructured files, then data services can be provided to potentially useful files, but unnecessary data services are applied to files that do not need them
Solution Approach 1:
The host device performs preliminary classification of unstructured files by deriving characteristics (such as file type, format, or content properties) before the storage system applies data services. This preliminary action enables the storage system to make informed decisions about which data services to apply, avoiding unnecessary processing while ensuring appropriate services are provided to files that need them.
2Productivity
If the storage system applies no data services to unstructured files, then storage system efficiency is maintained, but potentially useful data services are not applied to files that could benefit from them
Solution Approach 1:
The host device provides feedback to the storage system by communicating derived file characteristics (such as file type or content properties). This feedback mechanism enables the storage system to adjust its data service application strategy dynamically, applying services only to files that meet specific criteria while maintaining high efficiency by avoiding unnecessary processing of files that do not require such services.
3Measurement precision
If the host device derives detailed characteristics of files, then accurate classification is achieved, but additional processing time and resources are required
Solution Approach 1:
The host device derives only the specific characteristics that are relevant for data service classification (such as file type or content properties) rather than analyzing all possible attributes of each file. This selective approach to characteristic derivation maintains sufficient classification accuracy for making informed data service decisions while minimizing unnecessary processing time and resource consumption.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for block-level classification of unstructured data are provided herein. An example apparatus includes a host device comprising a processor coupled to memory, the host device being configured to communicate over a network with a storage system, and further being configured to: obtain a pointer to a page cache associated with an input-output operation for at least one page of unstructured data of a file; obtain an index node object of the file based at least in part on the pointer to the page cache; derive at least one characteristic of the file based at least in part on the obtained index node object; and provide an indication of the at least one characteristic to the storage system. The storage system determines whether to apply one or more functions to the unstructured data based on the indication.