Shared Dictionary Deduplication Engines Hash Distribution
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Solution Overview
Problem
Existing deduplication systems face inefficiencies in managing dictionaries across multiple deduplication engines, leading to increased memory usage and reduced storage capacity due to the need for each engine to maintain a full dictionary, especially in WAN environments.
Innovation Solution
A system and method for managing a network deduplication dictionary by dividing it among available deduplication engines using a hash function to distribute fingerprints, allowing selection of a deduplication engine based on the hash result, thereby reducing the need for each engine to maintain a full dictionary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each deduplication engine maintains a full dictionary, then data integrity and deduplication accuracy are ensured, but memory usage increases and storage capacity decreases
Solution Approach 1:
The patent divides the dictionary into multiple segments or partitions that can be distributed across different deduplication engines. Each engine maintains only a portion of the dictionary (e.g., dictionary segment 102, 104, 106) rather than a complete copy, thereby reducing individual memory requirements while collectively maintaining full dictionary coverage across the system.
Solution Approach 2:
The patent introduces a distributed architecture dimension where dictionary storage is organized across multiple engines rather than within a single engine. This spatial distribution allows the system to scale dictionary capacity horizontally across multiple devices, transforming the memory constraint from a vertical limitation into a horizontal scalability opportunity.
2Reliability
If each deduplication engine maintains a full dictionary, then deduplication accuracy is maintained, but storage capacity is reduced
Solution Approach 1:
The dictionary is segmented into multiple parts distributed across different engines, reducing the storage overhead on each individual device. This segmentation allows more storage capacity to be available for actual data while maintaining the complete dictionary functionality across the distributed system.
3Quantity of substance
If multiple deduplication engines share a dictionary, then memory usage is optimized, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components such as hash functions and coordination protocols that manage dictionary distribution and lookup across engines. These intermediaries provide structured mechanisms for engines to locate data segments without requiring complex peer-to-peer communication, thereby managing system complexity through well-defined interfaces and protocols.
4Quantity of substance
If data is distributed across different storage areas, then storage efficiency is improved, but caching effectiveness is reduced
Solution Approach 1:
The patent segments both the storage space and the dictionary structure, creating a consistent mapping between data segments and dictionary segments. This segmentation strategy allows the system to efficiently locate and cache relevant data segments based on dictionary lookups, maintaining caching effectiveness even with distributed storage through organized segment relationships.
Data Source
AI summary
In one embodiment, a system and method for managing a network deduplication dictionary is disclosed. According to the method, the dictionary is divided between available deduplication engines (DDE) in deduplication devices that support shared dictionaries. The fingerprints are distributed to different DDEs based on a hash function. The hash function takes the fingerprint and hashes it and based on the hash result, it selects one of the DDEs. The hash function could select a few bits from the fingerprint and use those bits to select a DDE.