Data Reduction Estimation via Representative Sampling
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Solution Overview
Problem
Existing data reduction methods in storage systems require full data processing for accurate estimation, which is time-consuming and inefficient for large storage systems, and do not effectively estimate data reduction effectiveness without compressing or deduplicating all data.
Innovation Solution
A method that selects a representative sample of data from large files in the storage system to estimate data reduction through data deduplication and compression, calculating full-file and chunk-wise deduplication ratios, and generating a combined data reduction estimate without requiring full data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full data processing is performed for accurate data reduction estimation, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent applies partial action by processing only a representative sample of data (e.g., first N bytes or M chunks) rather than the entire dataset. This sampling approach provides sufficiently accurate estimates of data reduction effectiveness while dramatically reducing processing time and resource consumption compared to full data processing.
Solution Approach 2:
The patent performs preliminary processing by computing hashes and identifying duplicates/compression opportunities in a sample portion of data before making estimation decisions. This preliminary analysis on sampled data enables quick assessment without committing resources to process all data.
2Measurement precision
If full data processing is performed for accurate data reduction estimation, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent processes only a partial sample of data (first N bytes or M chunks) to estimate data reduction metrics. This partial processing approach maintains acceptable estimation accuracy while improving productivity by avoiding the computational overhead of processing entire datasets.
Solution Approach 2:
The patent segments the data processing task by dividing the file system data into manageable portions (chunks) and selecting representative samples from different segments. This segmentation enables parallel processing and faster estimation compared to sequential full-data processing.
3Measurement precision
If data reduction estimation is performed on large storage systems, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent reduces system complexity by performing data reduction estimation on a partial sample rather than the entire large storage system. This approach maintains measurement precision for estimation purposes while significantly simplifying the computational infrastructure required.
Solution Approach 2:
The patent implements preliminary hashing and indexing of sampled data to enable efficient duplicate detection and compression estimation. This preliminary structuring of sample data reduces the complexity of subsequent analysis compared to processing raw entire datasets.
Data Source
AI summary
Embodiments of the present invention provide a system, method and computer program products for estimating data reduction in a file system. A method includes selecting a sample of all data from data files in the file system, wherein said sample represent a subset of all the data in the file system. The method further includes estimating a data reduction ratio by data deduplication for the file system based on said sample. The method further includes estimating a data reduction ratio by data compression for the file system based said sample. The method further includes generating a combined data reduction estimate for the file system based on said data compression estimate and said data deduplication estimate.