Extensible Data Deduplication System with Dynamic Node Scaling
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Solution Overview
Problem
Conventional data deduplication systems face scalability limitations due to increased data volumes, leading to performance issues and strain on network bandwidth, storage capacity, and data integrity, especially with large file sizes and exponential data growth.
Innovation Solution
The implementation of a computer-implemented method for data deduplication that includes a deduplication control node monitoring performance characteristics and adding additional deduplication nodes to the system when performance falls below a minimum threshold, with data blocks being migrated and rebalanced using modulo arithmetic operations on hash values to optimize storage and retrieval across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data volumes increase to meet storage capacity requirements, then storage capacity is improved, but network bandwidth becomes overloaded and performance deteriorates
Solution Approach 1:
The deduplication system segments data into blocks and distributes them across multiple nodes. Each node handles a portion of the deduplication workload, dividing the network traffic and processing load. This segmentation allows the system to scale storage capacity while distributing network bandwidth requirements across multiple paths, preventing single-point congestion.
Solution Approach 2:
The patent introduces a distributed multi-node architecture that adds a spatial dimension to the deduplication process. Instead of a single centralized system, data is spread across multiple nodes in a network, transforming a one-dimensional processing model into a multi-dimensional distributed system. This enables capacity scaling without proportional increases in network congestion at any single point.
2Quantity of substance
If additional storage capacity is added to handle exponential data growth, then storage capacity is improved, but capital expenditures, power consumption, and floor space increase
Solution Approach 1:
The system creates copies of data blocks across multiple nodes rather than storing every byte once. Through deduplication, identical data blocks are stored only once in the distributed system, with references maintained across nodes. This copying approach optimizes storage utilization and reduces the total physical storage capacity required, thereby lowering associated power consumption and infrastructure costs.
Solution Approach 2:
The patent changes the storage parameter from traditional per-byte storage to block-level deduplication with reference counting. By tracking data blocks and their occurrences across nodes, the system achieves higher storage density and reduces the physical storage footprint. This parameter change leads to reduced power consumption for storage hardware and cooling infrastructure.
3Quantity of substance
If data is stored across multiple nodes to improve scalability, then storage capacity is improved, but system complexity increases
Solution Approach 1:
Each node in the distributed system is designed with universal functionality to perform deduplication operations, data storage, and coordination tasks. The nodes are homogeneous and can replace each other, simplifying the overall system architecture. This multi-functionality at the node level reduces the need for specialized components and simplifies management despite the distributed nature of the system.
Solution Approach 2:
The system implements feedback mechanisms where nodes continuously report their status, data block locations, and performance metrics to coordinating components. This feedback enables automatic load balancing, failover, and optimization without manual intervention. The feedback loop simplifies system management by enabling automated responses to changing conditions in the distributed environment.
4Reliability
If conventional deduplication systems are used with large file sizes, then data deduplication is improved, but performance deteriorates due to processing time
Solution Approach 1:
Large files are segmented into smaller data blocks that can be processed in parallel across multiple nodes. This segmentation reduces the processing time for each individual block while maintaining the overall deduplication effectiveness. The distributed architecture enables concurrent processing of multiple blocks, significantly reducing total processing time compared to sequential processing of large files.
Solution Approach 2:
The system performs deduplication operations on data blocks rather than entire files, applying partial action to smaller units. This partial processing approach reduces the computational burden on any single node and accelerates overall processing speed. By working with smaller units, the system achieves faster processing times while maintaining comprehensive deduplication coverage.
Data Source
AI summary
A system and method for data deduplication is presented. A deduplication system includes a plurality of data deduplication nodes; and a data deduplication control node communicatively coupled to the plurality of data deduplication nodes. The deduplication control node performs the operations of performing data deduplication operations at a deduplication system having one or more deduplication nodes at which deduplication data blocks are stored; monitoring a performance characteristic of the deduplication system; and adding an additional deduplication node to the deduplication system when the performance characteristic indicates performance of the system below a predetermined minimum.