Adaptive Hash Tree Deduplication for Distributed File Systems
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Solution Overview
Problem
Distributed file systems (DFSes) face challenges in scaling hash-based deduplication due to conflicting requirements of high-performance and dynamic resource utilization, leading to inefficiencies in storage and memory usage, as well as limitations in detecting replicated blocks across a global hash tree.
Innovation Solution
A hash tree-based deduplication system that adapts resource utilization dynamically, using cryptographic hash functions to balance memory usage and deduplication overhead, allowing for flexible hash sizes and adaptive data structures that can be reconfigured at runtime, enabling efficient detection and removal of replicated data across a distributed file system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hash sizes are increased to reduce hash collision probabilities, then deduplication reliability is improved, but hash tree storage size increases linearly
Solution Approach 1:
The patent divides the hash tree into multiple levels, where each level stores hashes of a specific size. Lower levels use smaller truncated hashes while higher levels use full-sized hashes, segmenting the storage requirements across different precision levels to reduce overall memory consumption while maintaining deduplication reliability.
Solution Approach 2:
Different portions of the hash tree use different hash sizes based on their specific needs. Leaf nodes use truncated hashes for quick comparisons, while internal nodes use full hashes for definitive matching. This local optimization allows the system to use minimal storage where possible while ensuring reliability where necessary.
2Adaptability or versatility
If hash trees are scaled up from local nodes to DFS-wide deduplication, then deduplication coverage is improved, but memory usage and resource requirements increase
Solution Approach 1:
The patent implements dynamic hash tree structures that can be configured at runtime based on available resources. The system can adjust the number of hash levels, hash sizes, and tree depth dynamically, allowing DFS-wide deduplication coverage while adapting memory usage to available resources rather than requiring fixed large allocations.
Solution Approach 2:
The patent introduces multiple dimensions to the hash tree structure, including vertical levels with different hash sizes and horizontal partitioning across distributed nodes. This multi-dimensional approach allows the system to achieve DFS-wide coverage by distributing the hash tree across multiple nodes rather than concentrating it in single memory space.
3Ease of manufacture
If block-by-block comparison is used instead of hash trees, then implementation simplicity is improved, but deduplication performance and efficiency deteriorate
Solution Approach 1:
The patent applies preliminary hashing to data blocks before comparison, creating compact hash representations that can be compared efficiently. This preliminary action transforms the comparison task from examining entire blocks to comparing small hash values, dramatically improving deduplication efficiency while keeping the implementation approachable through familiar hashing operations.
Data Source
AI summary
Systems and methods are provided for preserving data in a data deduplication system. A hash tree-based deduplication system balancing memory utilization and duplication-related storage access overhead is disclosed. The system preferably relies on distributed file system infrastructure and the system modifies this infrastructure. The data structures may be adapted to accommodate file-block distribution properties at runtime, such as runtime-specializing the hash tree to detect replicated chunks.