Multi-Copy Data Storage Compression for Fast File Access
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Solution Overview
Problem
Cloud-based data storage systems face inefficiencies due to uniform compression methods across multiple copies of files, leading to increased energy usage and resource utilization when highly compressed files are frequently decompressed for access, as they do not account for varying system resource availability.
Innovation Solution
Implementing a data storage system where different copies of a file are compressed using distinct processes, prioritizing either faster decompression or storage space savings, allowing the system to select the most resource-efficient copy for decompression based on current system resources, and dynamically adjusting compression levels based on file access frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a high compression ratio algorithm is used to compress data objects, then storage space is conserved, but processing resources and latency increase
Solution Approach 1:
The patent segments the compression process by creating multiple copies of the same data object, each compressed using different compression algorithms with varying compression ratios and computational complexities. This allows the system to store multiple compressed versions (e.g., highly compressed copy, moderately compressed copy, lightly compressed copy) and select the appropriate copy based on current system resources and access patterns, thereby resolving the contradiction between storage efficiency and processing resource consumption.
2Reliability
If multiple copies of a file are stored for redundancy, then fault tolerance is improved, but energy usage increases when highly compressed files are frequently decompressed
Solution Approach 1:
The patent applies local quality by assigning different compression characteristics to different copies of the same data object stored at different locations (storage nodes). Each storage node can be configured with specific compression parameters appropriate for its role and access patterns. Frequently accessed copies can use lower compression ratios for faster decompression, while less frequently accessed copies can use higher compression ratios for space efficiency, thus reducing overall energy consumption while maintaining redundancy.
3Device complexity
If uniform compression process is applied to all copies, then system simplicity is maintained, but resource efficiency decreases when access patterns vary
Solution Approach 1:
The patent introduces dynamics by making the compression process adaptive rather than static. The system can dynamically select which compressed copy to use based on current system conditions, access patterns, and resource availability. Compression parameters can be adjusted over time based on monitoring of access frequencies and system performance, allowing the system to optimize resource efficiency while maintaining manageable complexity through automated decision-making.
Data Source
AI summary
A data storage system in which different copies of a data object (e.g., a file) can be compressed using different compression processes (e.g. different compression algorithms/processes and/or compression parameters), with some favoring faster decompression, while others favoring storage space savings. When a data object needs to be accessed, the copy of the data object that can be decompressed using minimal resource (computing and/or time) can be located and retrieved.