Dynamic Data Pattern Table for Storage Efficiency
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Solution Overview
Problem
Current storage systems face inefficiencies due to predefined data pattern tables that only include all-zero and all-one patterns, failing to capture frequently used data patterns in specific application scenarios, leading to suboptimal storage efficiency and increased processing burdens.
Innovation Solution
A method to dynamically update a data pattern table by analyzing incoming IOs and recording frequently used data patterns, allowing for a customized table that includes patterns other than all-zero and all-one, thereby enhancing data access efficiency and storage utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a predefined data pattern table with only all-zero and all-one patterns is used, then the system structure is simple, but storage efficiency is suboptimal and processing burdens increase
Solution Approach 1:
The patent transforms the static predefined data pattern table into a dynamic structure that is automatically updated based on actual data access patterns. The system monitors frequently accessed data patterns and dynamically adds them to the pattern table, enabling the structure to adapt to changing workloads and improve storage efficiency without manual intervention.
Solution Approach 2:
The system performs self-updating of the data pattern table by automatically identifying frequently accessed patterns from incoming I/O operations and incorporating them into the table. This self-service mechanism eliminates the need for external configuration or manual updates, allowing the system to continuously optimize its own performance.
2Ease of manufacture
If a predefined data pattern table with only all-zero and all-one patterns is used, then the implementation is simple, but processing burdens increase
Solution Approach 1:
The system performs preliminary actions by pre-processing incoming data patterns to identify frequently accessed patterns before they are stored. By proactively building the data pattern table with relevant patterns in advance, the system reduces the processing burden during actual data operations, as matching and compression can be performed more efficiently.
3Device complexity
If frequently accessed data patterns are not captured, then the data pattern table remains simple, but storage space is wasted
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors data access patterns and uses this information to update the data pattern table. By feeding back the actual usage patterns from the system operations, the table is dynamically optimized to capture frequently accessed patterns, thereby maximizing storage space utilization and reducing waste.
Data Source
AI summary
Techniques perform data input and output. For example, there is provided a technique for updating a data pattern table. The technique involves: during a time period for establishing or updating the data pattern table: receiving a data write request; making statistics of a frequency of data being written; and writing data frequently used and an identification of the data to a corresponding entry of the data pattern table based on a result of the statistics. Using such a technique, a data pattern table can be customized and data storage efficiency can be improved.