Distributed Data Deduplication via Local Index Segmentation
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Solution Overview
Problem
Traditional multi-node deduplication techniques scale poorly with large data collections and numerous nodes, leading to disproportionate network, memory, and storage resource consumption due to cross-node communications.
Innovation Solution
Implement a distributed data deduplication system where each node locally deduplicates data and routes data objects based on similarity hashes to efficiently store them on appropriate nodes, considering factors like similarity hash aggregation, file type, access frequency, and storage load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multi-node deduplication techniques are used to store large data collections across many nodes, then data deduplication capability is improved, but network, memory, and storage resources are consumed at a disproportionate rate due to cross-node communications
Solution Approach 1:
The system segments the globally accessible index into distributed local indexes maintained by individual nodes. Each node independently manages its own deduplication data structure, eliminating the need for a centralized index that requires extensive cross-node communications. This segmentation allows nodes to operate autonomously while still achieving system-wide deduplication through selective peer communications.
Solution Approach 2:
The patent implements local deduplication at each node by maintaining deduplication data structures locally rather than relying on a centralized global index. Each node performs deduplication operations using its local data structures, reducing the need for frequent cross-node communications and thereby decreasing network, memory, and storage resource consumption while maintaining effective deduplication capability.
2Measurement precision
If a globally accessible index is maintained to detect duplicate data segments across all nodes, then duplicate detection accuracy is improved, but cross-node communications consume resources at a disproportionate rate as data collections and nodes increase
Solution Approach 1:
The globally accessible index is segmented into multiple distributed local indexes, with each node maintaining its own deduplication data structure. This segmentation reduces the complexity of cross-node communications by allowing nodes to perform duplicate detection locally first, only communicating with peers when necessary, thereby maintaining duplicate detection accuracy while reducing overall system complexity.
Solution Approach 2:
The patent introduces local deduplication data structures as intermediaries between the centralized global index approach and direct peer-to-peer communication. These local structures act as mediators that cache and pre-process deduplication information, reducing the frequency and volume of direct cross-node communications needed for duplicate detection.
3Loss of energy
If deduplication is performed locally on each node without centralized coordination, then resource consumption is reduced, but node configuration flexibility and heterogeneous node support are improved
Solution Approach 1:
The system implements dynamic node configuration where each node can independently adjust its deduplication parameters and data structure based on local conditions. Nodes can be added or removed from the distributed system without requiring centralized reconfiguration, and each node adapts its behavior based on its local workload and resource availability, thereby maintaining low resource consumption while achieving high adaptability.
Solution Approach 2:
The patent designs a universal deduplication framework where the same local deduplication mechanism can be applied across heterogeneous nodes with different configurations, capacities, and workloads. Each node performs the same fundamental deduplication operations locally, but can handle different types of data and scale to different sizes, making the system universally applicable to diverse node configurations.
Data Source
AI summary
A computer-implemented method for distributed data deduplication may include (1) identifying a deduplicated data system, the deduplicated data system include a plurality of nodes, wherein each node within the plurality of nodes is configured to deduplicate data stored on the node, (2) identifying a data object to store within the deduplicated data system, (3) generating a similarity hash of the data object, the similarity hash representing a probabilistic dimension-reduction of the data object, (4) selecting, based at least in part on the similarity hash, a target node from the plurality nodes on which to store the data object, and then (5) routing the data object for storage on the target node based on the selection of the target node. Various other methods, systems, and computer-readable media are also disclosed.