Storage Descriptor Data Structures for Distributed Storage Analysis
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Solution Overview
Problem
Existing storage systems face inefficiencies and resource-intensive challenges in analyzing large, distributed datasets due to the need for aggregating data before analysis, making it difficult to extract valuable properties and manage complexity.
Innovation Solution
The implementation of Storage Descriptor Data Structures (SDDS) creates a virtual representation of storage systems by aggregating metadata and storage descriptors, using probabilistic data structures like HyperLogLog to estimate dataset characteristics, enabling efficient analysis and reporting without requiring the entire dataset to be moved or migrated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed data is aggregated before analysis, then complete dataset properties can be obtained, but resource consumption and time increase significantly
Solution Approach 1:
The patent extracts only the essential characteristics and metadata from the distributed dataset, storing them in a data lake, rather than aggregating the entire dataset. This allows analysis of dataset properties without moving or processing all underlying data, significantly reducing resource consumption while maintaining measurement precision for key metrics like data volume, diversity, and duplication.
Solution Approach 2:
The system performs preliminary analysis and characterization of distributed data, storing computed properties and metadata in advance in the data lake. When analysis is needed, these pre-computed properties are readily available, eliminating the need to re-aggregate and re-analyze the entire dataset, thus reducing both time and resource consumption.
2Measurement precision
If distributed data is aggregated before analysis, then complete dataset properties can be obtained, but processing time increases significantly
Solution Approach 1:
The patent extracts only the essential characteristics and metadata from the distributed dataset, storing them in a data lake, rather than aggregating the entire dataset. This allows analysis of dataset properties without moving or processing all underlying data, significantly reducing resource consumption while maintaining measurement precision for key metrics like data volume, diversity, and duplication.
Solution Approach 2:
The system performs preliminary analysis and characterization of distributed data, storing computed properties and metadata in advance in the data lake. When analysis is needed, these pre-computed properties are readily available, eliminating the need to re-aggregate and re-analyze the entire dataset, thus reducing both time and resource consumption.
3Loss of energy
If probabilistic data structures are used to represent datasets, then resource consumption is reduced, but measurement precision may be affected
Solution Approach 1:
The patent introduces metadata and sampled data characteristics as intermediary representations between the actual distributed dataset and the analysis process. These intermediaries capture essential properties like data volume, diversity, and duplication metrics, enabling accurate analysis without direct access to or processing of the entire dataset, thus reducing resource consumption while maintaining measurement precision.
4Productivity
If storage descriptor data structures are implemented, then analysis efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the storage system into distinct components: distributed storage nodes, a metadata service, and a data lake. Each component has a specific responsibility - nodes store data, the metadata service manages descriptors and characteristics, and the data lake stores aggregated properties. This segmentation improves analysis efficiency by enabling parallel operations and targeted data access, while the modular architecture actually manages complexity rather than increasing it.
Data Source
AI summary
Example implementations described herein are directed to a storage descriptor data structure that can represent characteristics of the dataset stored in an underlying volume without requiring the dataset in the volume to be migrated for processing. Such data structures are more compressed than the original data structure and can therefore facilitate storage allocation before the migration is conducted.