Content-Based Sampling for Data Deduplication Estimation
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Solution Overview
Problem
Existing data deduplication estimation methods are either too slow or inaccurate, particularly due to high computational complexity and latency, making it challenging to determine the effectiveness of deduplication in a given context.
Innovation Solution
A content-based sampling technique is employed, where a subset of fingerprints from a dataset is selected using an adaptive filter independent of frequency distribution, allowing for accurate deduplication estimation with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full dataset deduplication is performed to ensure accurate estimation, then measurement precision is improved, but productivity deteriorates due to high computational complexity and time consumption
Solution Approach 1:
The patent segments the full dataset into multiple fingerprint subsets, processing only a fraction of fingerprints (e.g., 1/M where M≥2) to generate deduplication estimates. This segmentation allows the system to achieve reasonable estimation accuracy while dramatically reducing computational complexity and processing time compared to analyzing the complete dataset.
Solution Approach 2:
The patent applies partial action by performing deduplication analysis on only a partial subset of fingerprints rather than the complete dataset. By selecting and processing a representative fraction of fingerprints (using sampling techniques), the system obtains sufficient estimation accuracy without the excessive computational resources required for full dataset analysis.
2Productivity
If random sampling is used to reduce computational complexity, then productivity is improved, but measurement precision deteriorates due to frequency distribution biases
Solution Approach 1:
The patent transforms the sampling approach by changing the selection parameter from random uniform sampling to content-based sampling. Instead of randomly selecting fingerprints regardless of content, the system uses sampling functions that select fingerprints based on their content characteristics, thereby eliminating frequency distribution biases and improving estimation accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent introduces sampling functions as intermediaries between the fingerprint dataset and the deduplication estimation process. These sampling functions act as mediators that selectively filter and choose fingerprints based on content-based criteria, ensuring representative sampling that avoids the biases inherent in pure random sampling methods.
3Productivity
If workload-type estimation is applied to reduce computational resources, then productivity is improved, but measurement precision deteriorates due to oversimplification
Solution Approach 1:
The patent replaces the mechanical/workload-type estimation approach with a content-based sampling system. Instead of relying on workload classification heuristics, the system directly analyzes fingerprint content characteristics through sampling functions, providing more accurate deduplication benefit predictions while maintaining processing efficiency.
Data Source
AI summary
The techniques introduced herein provide for systems and methods for estimating the effectiveness of utilizing a data deduplication process. More specifically, a content-based sampling approach for data deduplication estimation is described in which a subset of the scanned fingerprints of a dataset are included in a content-based sample that is used to determine an accurate deduplication estimate for a dataset (or volume).