Content-Aware Data Stream Deduplication for Storage Throughput
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Solution Overview
Problem
Conventional deduplication processes in storage management are time-consuming and resource-intensive, particularly during the partitioning and signature generation stages, leading to inefficient storage utilization and high processing overhead due to the use of fixed block sizes and large metadata/indexes that exceed memory capacity, resulting in unnecessary storage overhead and reduced throughput.
Innovation Solution
Implementing a content-aware deduplication method that generates variable-sized data streams based on the content of to-be-stored objects, allowing for matching with existing data streams, thereby reducing redundant storage and optimizing storage management performance by using specialized processing resources to assist general processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional deduplication process with fixed block sizes is used, then storage space can be reduced through deduplication, but the partitioning and signature generation phases consume large amounts of processing resources and time
Solution Approach 1:
The patent implements dynamic block sizing where the block size is not fixed but adapts based on the content characteristics of the data being processed. The system adjusts block sizes dynamically during the deduplication process to optimize both storage efficiency and processing performance, avoiding the resource-intensive fixed block size approach while maintaining effective deduplication
Solution Approach 2:
The system changes key parameters including block size, partitioning strategy, and processing thresholds based on data characteristics. By dynamically adjusting these parameters rather than using fixed values, the system reduces processing overhead while maintaining storage efficiency, directly addressing the contradiction between storage optimization and processing performance
2Device complexity
If fixed block sizes are used for deduplication, then the deduplication process is simpler to implement, but it leads to unnecessary storage overhead and reduced throughput
Solution Approach 1:
Rather than using static fixed block sizes, the system employs dynamic block sizing that adapts to data patterns. This dynamic approach, while slightly more complex than fixed sizing, dramatically improves throughput by avoiding unnecessary processing and storage of redundant data that would occur with fixed block boundaries
Solution Approach 2:
The deduplication system automatically adjusts its parameters based on the data it processes, making the complexity management self-service. The system self-optimizes block sizes and partitioning strategies without requiring external configuration, achieving high throughput while keeping the implementation complexity manageable through automated adaptation
3Measurement precision
If large metadata and indexes are used for comprehensive data tracking, then deduplication accuracy is improved, but the processing time and memory consumption increase significantly
Solution Approach 1:
The system extracts and processes only the essential metadata and index information needed for effective deduplication, rather than maintaining comprehensive metadata for all data. This selective extraction approach maintains deduplication accuracy by focusing on critical identification features while significantly reducing processing time and memory consumption
Solution Approach 2:
The system uses lightweight copies or representations of data (such as hash values or simplified metadata) instead of maintaining full copies of all data characteristics in memory. This copying strategy enables accurate deduplication matching while minimizing the time and resources required for metadata management and index operations
Data Source
AI summary
The present disclosure relates to a method, an electronic device, and a computer program product for storage management. According to an example, a method for storage management is provided, including: generating a to-be-stored target data stream based on a to-be-stored object, wherein the target data stream includes at least a part of the object, determining whether the target data stream matches at least one stored data stream that has been stored in a storage apparatus, wherein sizes of the target data stream and the at least one stored data stream depend on their respective content, and, if the target data stream does not match the at least one stored data stream, storing the target data stream in the storage apparatus. Therefore, the performance of storage management can be improved, and the storage costs can be reduced.