Compression Engine Tiering for Data Storage Latency Balance
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Solution Overview
Problem
Data storage systems face inefficiencies in data compression due to varying compression algorithms' effectiveness and latency, leading to suboptimal storage space usage and access times, as users manually select compression engines based on cost and expected needs without ensuring optimal compression ratios or access efficiency.
Innovation Solution
A data storage subsystem that selects and employs multiple data compression engines with different algorithms, dynamically determining the best engine for each data sample based on compression ratios and access rates, arranging engines in tiers by latency, and moving data between repositories based on access patterns to optimize compression and decompression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual selection of compression engines is used based on user expectations and cost, then device complexity is reduced, but data storage efficiency and access speed deteriorate
Solution Approach 1:
The system performs self-service by automatically evaluating and selecting the most appropriate compression engine for each data set without user intervention. The data handling system autonomously determines compression ratios, accesses patterns, and engine performance metrics to make optimal selection decisions, eliminating the need for manual user configuration while maximizing storage efficiency.
Solution Approach 2:
The system dynamically changes parameters including compression ratio thresholds, access pattern weights, and engine performance metrics based on real-time data characteristics. By adjusting these parameters according to actual data properties and system performance, the system optimizes storage efficiency without requiring complex manual configuration.
2Quantity of substance
If higher compression ratios are pursued, then storage space efficiency is improved, but compression latency increases
Solution Approach 1:
The system dynamically adjusts compression strategies based on real-time performance metrics and data characteristics. By continuously monitoring compression ratios, access patterns, and latency measurements, the system adapts its compression engine selection and parameters to optimize the balance between storage efficiency and compression speed for each specific data set.
Solution Approach 2:
The system applies partial compression actions by selecting compression engines and ratios that are sufficient rather than maximal. By evaluating actual data characteristics and access patterns, the system applies just enough compression to achieve acceptable storage efficiency without incurring excessive latency, avoiding the diminishing returns of over-compression.
3Ease of operation
If fixed compression policies are established, then ease of operation is improved, but adaptability to changing data patterns deteriorates
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring data access patterns, compression performance, and engine effectiveness. This feedback is used to dynamically adjust compression policies and engine selections, allowing the system to adapt to changing data patterns while maintaining simple initial configuration. The feedback loop enables automatic policy optimization without requiring complex user-defined rules.
Data Source
AI summary
A data storage subsystem having a plurality of data compression engines configured to compress data, each having a different compression algorithm. A data handling system is configured to determine a present rate of access to data; select at least one sample of data; determine the greatest degree of compression of said data compression engines; determine the compression ratios of the operated data compression engines with respect to the selected sample(s); compressing said selected at least one sample with a plurality of said data compression engines at said selected tier; operate a selected data compression engines with respect to the selected sample and determine the greatest degree of compression of the data compression engines; compress the data from which the sample was selected with one of the operated data compression engines determined to have the greatest degree of compression; and store the compressed data in data storage repositories.