Dynamic Compression Module Selection for Backup Storage
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Solution Overview
Problem
In large-scale backup storage management systems, manual correlation of content type with efficient compression processes is resource-intensive and inefficient, leading to suboptimal resource allocation due to the use of general compression processes that are not tailored to specific data types.
Innovation Solution
A dynamic compression module selection process that analyzes data sets to determine the most efficient compression module based on recent compression history, statistical data, and available resources, allowing for real-time selection and application of optimal compression algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual correlation of content type with compression process is used, then compression efficiency is improved, but resource consumption and time cost increase significantly
Solution Approach 1:
The system enables self-service by automatically analyzing data sets and selecting appropriate compression modules without manual intervention. The compression management module autonomously evaluates compression history, statistical data, and resource availability to make selection decisions, eliminating the need for administrators to manually correlate content types with compression processes.
Solution Approach 2:
The system implements dynamics by transitioning from static, manual compression module selection to dynamic, automated selection. The compression management module continuously adapts its decisions based on real-time analysis of compression history, statistical data, and current resource availability, allowing the system to respond flexibly to changing conditions.
2Device complexity
If general compression processes are used, then resource consumption is reduced, but compression efficiency decreases
Solution Approach 1:
The system applies local quality by selecting different compression modules tailored to specific data sets rather than using a single general compression process for all data. Each data set receives customized compression treatment based on its characteristics, compression history, and current resource conditions, optimizing compression efficiency for each local context.
Solution Approach 2:
The system implements parameter changes by adjusting compression module selection based on varying parameters such as compression history, statistical data, and resource availability. The compression management module dynamically changes which compression module is applied by modifying the selection parameters based on current system state and data characteristics.
3Productivity
If dynamic compression module selection is implemented, then compression-to-throughput ratio is optimized, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the compression management function into distinct modular components: the compression management module, compression history analysis, statistical data evaluation, and resource availability assessment. This modular architecture manages system complexity by organizing functions into separate, manageable segments that can operate independently but coordinate together.
Data Source
AI summary
A computer-implemented method for compressing a data set, the method comprising receiving a first data block of the data set, selecting automatically by a compression management module a compression module from a plurality of compression modules to apply to the first data block based on projected compression efficacy or resource utilization, and compressing the first data block with the selected compression module to generate a first compressed data block.