Cross-Enclosure Data Device Grouping for Archival Storage
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Solution Overview
Problem
Commercial enterprises face high costs in managing large volumes of data due to the need for multiple storage tiers, with archival storage systems being particularly challenging to keep cost-effective while maintaining high storage density, often requiring the sacrifice of computing capabilities.
Innovation Solution
An archival data storage system utilizing multiple-data-storage-devices cartridges with low-cost, low-quality components designed for 'cold' data, implementing a data range API and parallel processing to scale capacity and throughput, and employing deduplication, erasure coding, and token-based cascade staging to optimize storage efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional solutions are used to drive down the cost of archival storage systems, then storage costs are reduced, but computing capabilities are sacrificed by removing storage efficiency services and reducing storage access bandwidth
Solution Approach 1:
The system segments data storage across multiple enclosures, each containing multiple data storage devices. Data is divided into fragments and distributed across these enclosures, allowing the system to achieve high storage density while maintaining computing capabilities through coordinated access to segmented data portions.
Solution Approach 2:
The patent introduces an intermediary mechanism that manages data fragments across multiple enclosures, enabling efficient data maintenance operations. This intermediary layer coordinates access to data fragments stored in different enclosures, preserving computing capabilities while achieving high storage density through distributed storage.
2Quantity of substance
If data is stored across multiple enclosures to increase storage capacity, then storage density is improved, but data maintenance complexity increases due to the need for coordinated access across enclosures
Solution Approach 1:
Data is segmented into fragments and distributed across multiple enclosures, each enclosure storing a portion of the overall data set. This segmentation enables scalable storage capacity while the system manages complexity through organized fragment distribution and retrieval mechanisms.
Solution Approach 2:
The system employs homogeneous data fragment structures across different enclosures, where each enclosure stores data fragments in a consistent format. This homogeneity simplifies data maintenance operations by allowing uniform access patterns and reducing the complexity of coordinating operations across heterogeneous storage systems.
3Quantity of substance
If storage efficiency services are removed to reduce costs, then storage costs are reduced, but storage efficiency deteriorates
Solution Approach 1:
The system implements self-service storage efficiency mechanisms where data fragments are automatically managed across enclosures without requiring complex centralized control. The distributed architecture enables automatic data maintenance, deduplication, and efficiency optimizations at the enclosure level, reducing overall storage costs while maintaining storage efficiency through decentralized intelligence.
Data Source
AI summary
Techniques for operating a storage front-end system are disclosed. The techniques include identifying a synchronous group of data storage devices across two or more enclosures, each of which comprise a plurality of data storage devices. Data across the data storage devices is accessible by a storage front-end system as an aggregate memory space. The techniques further include sending an activation request to the enclosures to synchronously activate the data storage devices in the synchronous group and performing a data maintenance task in the aggregate memory space of the data storage devices.