Storage Data Compression Prioritization Algorithm
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Solution Overview
Problem
Current data compression techniques in enterprise storage systems are often unsophisticated and non-optimal, failing to effectively utilize storage capacity by not prioritizing data elements based on expected benefits and available processing resources.
Innovation Solution
The method involves prioritizing data elements based on expected compression benefits and available processing resources, selecting an appropriate data compression algorithm to maximize cumulative benefits, and optimizing data compression in storage arrays with multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to all data elements uniformly, then storage capacity utilization improves, but processing resources are wasted on low-benefit data elements
Solution Approach 1:
The patent applies different compression strategies to different data elements based on their individual characteristics and expected compression benefits. High-priority data elements with high expected compression ratios receive aggressive compression, while low-priority elements with low expected compression ratios are excluded from compression or receive minimal processing. This local differentiation optimizes the balance between storage capacity utilization and processing resource consumption.
2Quantity of substance
If aggressive compression algorithms are used, then data reduction ratio improves, but processing time and resource consumption increase
Solution Approach 1:
The patent applies compression selectively rather than universally. It identifies and compresses only those data elements that are expected to achieve significant compression ratios (high-priority elements), while leaving other elements uncompressed or minimally compressed. This partial action approach achieves substantial overall data reduction without incurring the full processing cost of applying aggressive compression to all data.
Solution Approach 2:
The patent dynamically adjusts compression parameters and algorithm selection based on the expected compression benefit of each data element. For high-priority elements, more aggressive compression parameters are applied; for low-priority elements, compression is reduced or eliminated. This parameter adaptation optimizes the trade-off between data reduction ratio and processing time.
3Quantity of substance
If compression is applied to low-priority data elements, then storage capacity utilization improves, but cumulative benefit is reduced due to diminishing returns
Solution Approach 1:
The patent performs preliminary assessment of each data element's expected compression benefit before applying compression. By calculating or estimating the expected compression ratio in advance, the system can prioritize elements that will yield the highest cumulative benefit. This preliminary action ensures that limited processing resources are allocated to data elements that will provide the greatest overall compression benefit, maximizing productivity.
Data Source
AI summary
Intelligently compressing data in a storage array that includes a plurality of storage devices, including: prioritizing, in dependence upon an expected benefit to be gained from compressing each data element, one or more data elements; receiving an amount of processing resources available for compressing the one or more of the data elements; and selecting, in dependence upon the prioritization of the one or more data elements and the amount of processing resources available for compressing one or more of the data elements, a data compression algorithm to utilize on one or more of the data elements.