User-Specific Data Compression Level Selection for Latency Balance
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Solution Overview
Problem
Current data compression methods often use predetermined compression levels without differentiation for users, leading to inefficient resource utilization and increased latency, as higher compression levels consume more resources and introduce longer latency, while lower levels may not provide sufficient space savings.
Innovation Solution
A method to determine a target compression level for each user by compressing data using multiple compression levels of a compression algorithm, analyzing compression ratios and latencies, and selecting the optimal level based on user-specific data characteristics, allowing for tailored compression settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If higher compression levels are used to save storage space, then storage space is reduced, but resource consumption increases and latency increases
Solution Approach 1:
The system dynamically changes compression parameters by testing multiple compression levels and selecting the optimal level for each user based on their data characteristics. This resolves the contradiction by adapting the compression level parameter rather than using a fixed high compression level for all users, thereby saving storage space while avoiding excessive resource consumption.
Solution Approach 2:
The system performs self-optimization by automatically determining the best compression level for each user through testing and analysis, without requiring manual intervention. This enables the system to find the optimal balance between storage space savings and resource consumption autonomously.
2Volume of stationary object
If higher compression levels are used to save storage space, then storage space is reduced, but latency increases
Solution Approach 1:
The system dynamically adjusts the compression level parameter based on user-specific analysis, selecting the lowest compression level that achieves sufficient space savings for each user. This resolves the latency contradiction by avoiding unnecessarily high compression levels that would increase processing time.
Solution Approach 2:
The system applies compression selectively by determining the minimum necessary compression level for each user rather than applying maximum compression uniformly. This partial action approach achieves adequate space savings without the excessive latency introduced by always using the highest compression level.
3Device complexity
If predetermined compression levels are used without user differentiation, then system complexity is reduced, but resource utilization efficiency decreases
Solution Approach 1:
The system introduces dynamic parameter adjustment by determining optimal compression levels for each user based on their data characteristics. This resolves the contradiction by allowing the system to maintain simplicity in implementation while achieving high resource utilization efficiency through adaptive parameter selection.
Solution Approach 2:
The system transitions from static predetermined compression levels to dynamic user-specific compression level selection. This dynamic approach improves resource utilization efficiency while the automation of the process keeps the added complexity manageable.
4Productivity
If user-specific compression levels are determined through testing multiple levels, then resource utilization efficiency is improved, but processing time increases
Solution Approach 1:
The system performs preliminary testing with sample data to determine optimal compression levels before actual data compression. This preliminary action allows the system to establish user-specific compression parameters in advance, improving resource utilization efficiency while minimizing the time impact on actual data processing operations.
Solution Approach 2:
The system tests only partial data samples rather than processing entire datasets during the optimization phase. This partial testing approach achieves sufficient accuracy in determining optimal compression levels while significantly reducing the processing time required for the initial setup.
Data Source
AI summary
Embodiments of the present disclosure provide a method, electronic device and a computer program product for processing data. The method comprises determining target data that are used for determining a target compression level for a user. The method also comprises compressing at least part of the target data using a plurality of compression levels of a compression algorithm, respectively, to obtain a plurality of compression ratios and a plurality of compression latencies corresponding to the plurality of compression levels. The method further comprises determining the target compression level for the user for compressing data of the user data based on the plurality of compression ratios and the plurality of compression latencies.