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Data Clustering for DeepSketch . Existing clustering algorithms are unsuitable for DeepSketch
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Classroom Contents
DeepSketch - A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression
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- 1 Intro
- 2 Executive Summary
- 3 Data Reduction in Storage Systems
- 4 Post-deduplication Delta Compression Combines three different data-reduction approaches
- 5 Overview of Post-Deduplication Delta Compression
- 6 Lossless Compression
- 7 Key Challenge: Reference Search How to find a good reference block for an incoming data block across a wide range of stored data at low cost
- 8 Limitations of Existing Techniques - Provide significantly lower data-reduction ratios than the optimal
- 9 DeepSketch: Key Idea Use the learning-to-hash method for sketch generation A promising machine learning (ML).-based approach for the
- 10 DeepSketch: Challenges Lack of semantic information
- 11 Data Clustering for DeepSketch . Existing clustering algorithms are unsuitable for DeepSketch
- 12 Post-Processing for Training Data Set Non-uniform distribution of data blocks across the clusters
- 13 Evaluation Methodology Compared data-reduction techniques
- 14 Overall Data-Reduction Benefits
- 15 Performance Overhead
