Storage Server Compression Routing by Data Entropy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data compression in storage servers can be time-consuming, leading to degraded performance due to varying user data types, and existing methods may introduce side effects or inefficiencies.
Innovation Solution
A method and apparatus for data-compression management in storage servers using entropy detection to classify and compress data sets, employing multiple data compression modules with different capabilities, and recording address mapping information to optimize storage space usage without introducing significant side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed on all user data, then storage space is saved, but processing time increases and performance degrades
Solution Approach 1:
The patent changes the parameter of data classification by using entropy detection values to categorize data into different types (first type with low entropy, second type with high entropy). This allows selective compression based on data characteristics, saving storage space for compressible data while avoiding time-consuming compression of already-compressed or random data, thus resolving the contradiction between storage efficiency and processing speed.
Solution Approach 2:
The patent applies different compression treatments to different portions of data based on their entropy characteristics. First type data (low entropy) undergoes compression while second type data (high entropy) is stored directly. This local differentiation approach ensures that compression is applied only where beneficial, maintaining high processing speed while achieving storage space savings.
2Productivity
If entropy detection and classification is performed on all data, then compression efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct modules: an entropy detection module that calculates entropy values, a classification module that categorizes data based on entropy thresholds, and compression modules that handle different data types differently. This segmentation makes the complex system manageable and allows each component to perform its specific function efficiently, reducing overall system complexity while maintaining high compression efficiency.
Data Source
AI summary
A method for performing data-compression management in a storage server may include: receiving data from a host device; performing entropy detection on a plurality of sets of partial data to generate entropy detection values of the plurality of sets of partial data, respectively; classifying the plurality of sets of partial data according to the entropy detection values of the plurality of sets of partial data, respectively, to perform data compression on at least one portion of the plurality of sets of partial data through a plurality of data compression modules, respectively, wherein the plurality of data compression modules correspond to different compression capabilities, respectively; and storing the plurality of sets of partial data into at least one storage device of the storage server and recording address mapping information of the plurality of sets of partial data, respectively. An associated apparatus is also provided.