Data Storage Compression Prediction for Lower Processing Overhead
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Solution Overview
Problem
Modern storage systems face inefficiencies due to varying compression ratios for different data sets, leading to unnecessary processing overheads and minimal storage space savings when compressing data prior to storage.
Innovation Solution
A method and apparatus that utilize a prediction model to determine if data can be compressed by a predetermined threshold, allowing for selective compression and storage, thereby reducing processing overheads and optimizing storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression operation is executed on all data prior to storage, then storage space is reduced, but processing overhead increases for data that cannot be compressed effectively
Solution Approach 1:
The patent applies preliminary action by using a prediction model to assess data compressibility before executing the compression operation. The system predicts whether data will achieve a compression ratio above a threshold, and only performs compression on data likely to benefit, thereby avoiding unnecessary processing overhead while still achieving storage space reduction where applicable
Solution Approach 2:
The patent changes the parameter of compression ratio threshold to determine whether compression should be applied. By setting a predetermined threshold and using the prediction model to evaluate against this parameter, the system dynamically decides whether to compress data, optimizing the balance between storage space savings and processing overhead
2Quantity of substance
If compression operation is executed on all data prior to storage, then storage space is saved, but unnecessary processing occurs for incompressible data
Solution Approach 1:
The prediction model performs preliminary assessment of data compressibility before the actual compression operation. This preliminary action identifies incompressible data patterns, allowing the system to skip compression for such data and thereby save processing time while still achieving storage space optimization for compressible data
3Productivity
If prediction model is used to determine compressibility, then processing overhead is reduced, but system complexity increases
Solution Approach 1:
The patent replaces the mechanical compression operation with a predictive assessment using a machine learning model. Instead of always performing the mechanical compression process, the system uses the prediction model to substitute and determine whether compression is worthwhile, improving processing efficiency while the model complexity is managed through efficient architecture design
Data Source
AI summary
Techniques involve storing data. In particular, such techniques involve: obtaining first data to be stored; determining whether the first data is able to be compressed in a compression ratio exceeding a predetermined threshold; and storing, based on the determined result, the first data into a storage device. Accordingly, such techniques can execute corresponding processing for data in a predicted compression ratio, so as to store the data into a storage device. In this manner, such techniques can significantly cut down the overheads for processing data while minimizing a storage space required for storing data.