Adaptive Compression of System State Data by Predicted Usage
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Solution Overview
Problem
Existing information processing systems face challenges in efficiently managing storage by applying uniform compression levels to system state information, leading to unnecessary expansion and improper utilization of storage resources.
Innovation Solution
The implementation of machine learning models to predict the usage frequency of system state information, allowing for the determination of optimal compression levels based on predicted usage, thereby reducing storage requirements and database overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform compression levels are applied to all system state information, then the storage management process is simple, but storage resources are improperly utilized and unnecessary expansion occurs
Solution Approach 1:
The patent applies different compression levels to different data blocks based on their individual access patterns and characteristics. Instead of uniform compression, each data block is evaluated separately and assigned an appropriate compression level (first, second, or third level), optimizing storage efficiency for each specific data type while maintaining overall system simplicity.
2Quantity of substance
If higher compression levels are applied to reduce storage requirements, then storage efficiency improves, but data access speed decreases
Solution Approach 1:
The patent dynamically adjusts compression levels based on predicted access patterns. Data blocks with low predicted access frequency are compressed at higher levels, while frequently accessed blocks use lower compression. The system periodically re-evaluates and re-compresses data blocks as access patterns change, optimizing both storage efficiency and access performance over time.
3Quantity of substance
If data is compressed before storage to reduce database overhead, then storage efficiency improves, but decompression time increases
Solution Approach 1:
The patent changes the compression parameter (compression level) based on the predicted usage frequency of each data block. By adjusting this parameter dynamically, the system achieves optimal balance between storage efficiency and decompression time, applying stronger compression only when it will not significantly impact performance.
Data Source
AI summary
An apparatus comprises a processing device configured to receive system state information corresponding to one or more devices, to predict a usage frequency of the system state information using one or more machine learning models, and to determine, based at least in part on the usage frequency, a compression level for storage of the system state information. The compression level is applied to the system state information to generate at least one compressed file for transmission to a database.