Data Extent Migration Between Compressed Ranks and Read Latency
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Solution Overview
Problem
Existing storage systems struggle to efficiently manage data migration between compressed and non-compressed ranks in RAID configurations, leading to inefficiencies in storage device utilization and performance.
Innovation Solution
A scoring mechanism is employed to determine the optimal migration of data extents between compressed and non-compressed ranks based on factors such as storage capacity, compression ratios, and read/write statistics, allowing for dynamic adjustment of data placement to improve storage efficiency and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in compressed ranks, then storage capacity is improved, but read latency increases
Solution Approach 1:
The patent implements dynamic data migration between compressed and non-compressed ranks based on real-time scoring. The storage controller continuously evaluates data characteristics, access patterns, and compression ratios, then migrates extents between rank types to optimize the balance between storage capacity and read performance. This dynamic adjustment allows the system to adapt to changing workloads and data properties.
Solution Approach 2:
The patent applies different storage characteristics to different data extents based on their individual properties. Rather than uniformly storing all data in compressed or non-compressed ranks, the system evaluates each extent's compression ratio, access frequency, and data characteristics, then places each extent in the most appropriate rank type (compressed or non-compressed) to optimize both capacity utilization and read performance for that specific data.
2Productivity
If data migration is performed frequently, then storage efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback-driven migration system where the storage controller continuously monitors data characteristics, access patterns, and compression performance, then uses this feedback to make informed migration decisions. The scoring mechanism evaluates multiple factors including compression ratios, read/write statistics, and storage capacity utilization to determine optimal migration timing and targets, reducing unnecessary migrations while maintaining storage efficiency.
Solution Approach 2:
The patent changes the state of data extents by migrating them between compressed and non-compressed ranks based on evaluated parameters. The system monitors parameters such as compression ratios, access frequency, and storage utilization, then triggers migrations when parameter thresholds are met or when scoring indicates improved efficiency would result, allowing controlled adjustment of storage characteristics without constant migration.
3Quantity of substance
If compression is applied to data, then storage capacity is improved, but processing cycles increase
Solution Approach 1:
The patent applies compression selectively rather than universally. The scoring mechanism evaluates each data extent's compression potential and actual compression ratio achieved, then determines whether compression is worthwhile for that specific extent. This partial application of compression to only those extents that benefit most reduces overall processing overhead while maintaining storage capacity benefits for compressible data.
Data Source
AI summary
A score is generated for each extent in a storage device based on a plurality of factors including storage capacities, expected compression ratios, and statistics on reads and writes. An extent is moved between a compressed rank and a non-compressed rank based on the generated score.