Tiered Compression Hardware Selection for Mixed Data Sets
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Solution Overview
Problem
Current data storage systems lack an efficient method to determine the optimal compression algorithms for different data sets, leading to suboptimal data reduction and storage efficiency.
Innovation Solution
A method that involves determining multiple tiers of compression algorithms based on collected compression information from various data sets, recommending the use of specific hardware devices for each tier, and licensing considerations to achieve the best compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single compression algorithm is used for all data sets, then the system complexity is low, but the compression ratio is suboptimal for different data types
Solution Approach 1:
The patent segments compression algorithms into multiple tiers based on their performance characteristics and resource requirements. Tier 1 contains algorithms suitable for general-purpose compression, while Tier 2 contains algorithms optimized for specific data types achieving higher compression ratios. The system automatically selects the appropriate tier based on data set analysis, resolving the contradiction between maintaining low system complexity and achieving optimal compression ratios for different data types.
Solution Approach 2:
The system dynamically changes compression parameters by selecting different algorithms from Tier 1 or Tier 2 based on data set characteristics. The compression engine analyzes data patterns and adjusts the compression approach accordingly, enabling optimal compression ratios without requiring manual configuration or increasing overall system complexity.
2Productivity
If multiple compression algorithms are implemented to achieve optimal compression for different data sets, then the compression efficiency is improved, but the hardware resource requirements increase
Solution Approach 1:
The patent implements a dynamic compression engine that adapts its behavior based on data set characteristics. Rather than statically allocating multiple hardware compression devices, the system dynamically selects from Tier 1 and Tier 2 algorithms, adjusting resource utilization in real-time. This dynamic approach maintains high compression efficiency while optimizing hardware resource usage by only activating the necessary compression tier for each data set.
Solution Approach 2:
The system applies local quality by matching specific compression algorithms to specific data types. Tier 1 algorithms are used for data types where they provide sufficient compression, while Tier 2 algorithms are selectively applied to data types that benefit from their specialized compression approaches. This localized optimization achieves high compression efficiency without requiring all hardware resources to be simultaneously active.
3Loss of substance
If compression algorithms are selected without analysis, then the decision-making process is simple, but the storage efficiency is suboptimal
Solution Approach 1:
The patent implements preliminary action by pre-analyzing data sets and collecting compression hints before final compression decisions are made. The compression engine evaluates data patterns, identifies suitable algorithms from Tier 1 or Tier 2, and prepares compression strategies in advance. This preliminary analysis enables optimal storage efficiency while automating the decision-making process, reducing the perceived complexity for users.
Solution Approach 2:
The system employs feedback mechanisms where compression performance is continuously monitored and used to refine future compression decisions. The compression engine learns from previous compression operations and adjusts its algorithm selections accordingly, improving storage efficiency over time while maintaining automated decision-making that does not require user intervention.
Data Source
AI summary
Tiers of compression algorithms may be determined using compression information collected regarding compression ratios achieved for data sets using compression algorithms. Each tier may meet specified criteria regarding expected compression ratios achieved for a specified portion or number of data sets. Compression algorithms of each tier may be implemented by a different hardware device that may include hardware accelerators for the algorithms of the tier. Different tiers, and thus different hardware devices, achieve different levels of compression. A recommendation may be provided using compression information collected, such as from one of the hosts, regarding which hardware device to use for compression. The recommendation may be to purchase a license to use or whether to purchase a particular hardware device for compression. Compression information may be collected by a host that issues tagged I/Os providing a hint regarding what compression algorithm to use for the particular I/O operation data.