Flash Storage Zoning by Data Similarity for Better Compression
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Solution Overview
Problem
Conventional storage systems face inefficiencies in data compression due to storing dissimilar types of data together, which reduces the effectiveness of identifying redundancies and thus the efficiency of compression operations.
Innovation Solution
Storing data with similar properties, such as language or type, in the same data segment to enhance the efficiency of compression operations by minimizing computing overhead and improving redundancy identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If dissimilar types of data are stored together in data segments, then storage capacity is utilized efficiently, but compression efficiency deteriorates due to reduced redundancy identification
Solution Approach 1:
The patent divides the storage system into multiple data segments where each segment stores data with similar properties (e.g., same language, same file type). This segmentation allows compression algorithms to work more effectively on homogeneous data while maintaining efficient storage capacity utilization across the entire storage system.
Solution Approach 2:
The patent applies different storage strategies to different data segments based on their specific properties. Each segment is optimized for its particular data type, allowing local compression efficiency improvements without sacrificing overall storage capacity utilization. For example, text segments use text-specific compression while binary segments use binary-specific compression.
2Productivity
If data is organized by similarity properties, then redundancy identification improves, but computing overhead increases due to property analysis
Solution Approach 1:
The patent performs property analysis and data classification during the data ingestion phase, before compression operations. By pre-organizing data into segments based on properties like language, file type, or content characteristics, the system avoids repeated analysis during compression, thereby improving redundancy identification efficiency while minimizing additional computing overhead during write operations.
Solution Approach 2:
The system automatically analyzes data properties and performs self-organization into appropriate segments without requiring manual intervention or complex external processing. The compression algorithms themselves benefit from the pre-organized structure, identifying redundancies more efficiently while the system manages the property analysis overhead through automated, lightweight property detection mechanisms.
Data Source
AI summary
A method of storing data is provided. The method includes receiving a plurality of data blocks provided to a hyperscaler system. The method also includes determining a corresponding property for each data block of the plurality of data blocks. The method further includes identifying a set of data blocks from the plurality of data blocks. Each data block of the set of data blocks is associated with a first property. The method further includes storing the set of data blocks in a first zone of a zoned storage system, based on the first property.