Dynamic Iteration Schemes for Data Deduplication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data deduplication systems require large index tables to identify duplicate data blocks across storage extents, consuming significant memory and storage resources, and lack the ability to dynamically change iteration schemes, leading to inefficiencies and potential data staleness during the deduplication process.
Innovation Solution
A method for selecting and dynamically changing iteration schemes based on evaluation criteria to optimize data deduplication, focusing on iterating over data blocks with a high probability of being deduplicated, thereby reducing resource consumption and improving deduplication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large index tables are used to identify duplicate data blocks across storage extents, then deduplication accuracy is improved, but memory and storage resources are significantly consumed
Solution Approach 1:
The patent segments the data storage into smaller units called data blocks and processes them in iterations through storage extents. Instead of creating a single large index table containing all data blocks, the system builds index tables incrementally by processing storage extents in segments, thereby reducing the memory and storage resources required at any given time while maintaining deduplication accuracy.
Solution Approach 2:
The patent implements dynamic iteration schemes where the system can change the order and manner of processing storage extents based on evaluation criteria. The iteration scheme is not fixed but adapts dynamically to optimize the balance between deduplication accuracy and resource consumption, allowing the system to adjust how index tables are built and updated during the deduplication process.
2Ease of operation
If conventional fixed iteration schemes are used for data deduplication, then the process is simple to implement, but deduplication efficiency is reduced and data staleness may occur
Solution Approach 1:
The patent transforms the fixed iteration scheme into a dynamic one that can change based on evaluation criteria. The system monitors deduplication progress and resource usage, then adjusts the iteration order and strategy accordingly. This dynamic adaptation improves deduplication efficiency by focusing resources on the most promising data regions while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously evaluates the deduplication process and adjusts the iteration scheme based on observed performance and resource consumption. This feedback loop enables the system to learn from previous iterations and optimize future processing, thereby improving efficiency without complicating the overall implementation.
3Reliability
If all data blocks are processed sequentially to ensure complete deduplication, then deduplication thoroughness is improved, but processing time increases and data may become stale
Solution Approach 1:
The patent divides the complete data set into multiple storage extents that are processed in iterations. Instead of sequentially processing all data blocks from start to finish, the system segments the processing into parallelizable units and iterates through them in optimized orders, reducing total processing time while maintaining thoroughness through multiple passes over different portions of the data.
Solution Approach 2:
The patent implements dynamic iteration schemes that adapt the processing order and strategy based on evaluation criteria. The system can prioritize certain storage extents or data regions based on their likelihood of containing duplicates or their importance, thereby reducing the time required to achieve thorough deduplication by focusing processing efforts where they are most effective.
Data Source
AI summary
A method is used in selecting iteration schemes for deduplication. An iteration scheme is selected. Based on the selection, sets of data are iterated. Based on the iteration, a deduplicating technique is applied to the sets of data. Based on the results of applying the deduplicating technique, the iteration scheme is changed. Based on an evaluation of a set of criteria, the iteration scheme is changed.