Rating Hash Table for Backup Data Deduplication
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Solution Overview
Problem
The existing backup processes for physical or virtual machines consume excessive resources and cause high network load due to the large amount of data transmitted during the backup process, particularly when caching processes are used, leading to performance issues and inefficiencies.
Innovation Solution
A rating hash table system is implemented where input data is inserted based on a hash function, with ratings adjusted for popularity and aging, allowing for efficient data deduplication and reduced data transmission by prioritizing frequently used data and eliminating redundant data transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If caching processes are used during backup, then data transmission can be reduced, but memory consumption increases excessively
Solution Approach 1:
The hash table is divided into multiple buckets, and each bucket is divided into multiple blocks. This segmentation allows the system to manage data in smaller, more manageable units, reducing the memory overhead required for tracking and managing the entire cache at once while still achieving effective data deduplication.
Solution Approach 2:
Different blocks within buckets have different ratings that reflect their local importance or frequency of access. By assigning local quality metrics (ratings) to different blocks, the system can prioritize which data to keep in memory and which to evict, optimizing the balance between memory usage and data transmission reduction.
2Productivity
If a rating hash table with multiple buckets and blocks is implemented, then data deduplication efficiency improves, but device complexity increases
Solution Approach 1:
The hash table is segmented into buckets and blocks to enable efficient data deduplication. Each bucket contains multiple blocks, and the segmentation allows for localized management of data elements, improving deduplication efficiency by enabling finer-grained control over data storage and retrieval operations.
Solution Approach 2:
The system pre-allocates and structures the hash table with multiple buckets and blocks before data insertion begins. This preliminary structuring establishes an organized framework that facilitates efficient data placement and deduplication operations, reducing the complexity of dynamic structure management during runtime.
3Loss of energy
If frequent data is prioritized for caching, then network load reduces, but the complexity of tracking and managing ratings increases
Solution Approach 1:
The system assigns local quality ratings to different blocks based on their frequency of access or importance. This local quality differentiation enables the system to prioritize caching of frequently accessed data, reducing network load by avoiding retransmission of common data while managing rating complexity through localized rather than global tracking.
Solution Approach 2:
The rating mechanism provides feedback about data access patterns, allowing the system to adaptively prioritize which data to cache. By continuously updating ratings based on access frequency and using this feedback for caching decisions, the system reduces network load while managing complexity through a self-regulating mechanism.
Data Source
AI summary
A method for reducing an amount of data transmitted during a backup process is described. The method may include receiving input data to insert into a rating hash table during the backup process. The method may further include selecting, based on a hash function, a bucket of the rating hash table in which the input data will be inserted, the bucket including a plurality of blocks. The method may also include, in response to determining that the input data has already been inserted in one of the plurality of blocks, increasing a rating corresponding to the one of the plurality of blocks by a popularity rating increment. The method may additionally include, in response to determining that the input data has not already been inserted in one of the plurality of blocks, determining a first block with a smallest rating from the plurality of blocks.