N-Space Storage Map Translation for Enterprise Data Deduplication
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Solution Overview
Problem
Current storage solutions for enterprise systems are fragmented and underperforming, leading to inefficiencies in storage capacity, power consumption, and latency, and hinder the adoption of newer technologies like solid-state devices due to redundant and duplicative functionalities across multiple layers in the storage stack.
Innovation Solution
A storage map is used to translate storage requests with multiple fields into physical addresses by traversing a map table with root, inner, and leaf nodes, allowing for condensed storage services and optimized handling of dense and sparse information through hashed and sorted storage areas, enabling efficient deduplication and snapshotting without data copying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple storage services are implemented as separate layers in the storage stack, then each service can be independently optimized, but the overall system complexity increases and performance deteriorates due to redundant operations
Solution Approach 1:
The patent combines multiple storage services (snapshots, clones, deduplication, compression) into a single integrated storage map data structure. Instead of having separate software layers for each service, all these functionalities are merged into one unified map that handles multiple operations simultaneously, reducing the number of software layers and improving performance.
Solution Approach 2:
The storage map is designed as a universal data structure that performs multiple functions: it handles address translation, snapshot creation, clone operations, deduplication, and compression all through a single unified mechanism. This multi-functional approach eliminates the need for separate specialized layers for each storage service.
2Reliability
If data is copied to create snapshots and clones, then data integrity is ensured, but storage capacity is reduced and time is lost due to duplicative data copying
Solution Approach 1:
The patent uses a form of copying where instead of physically duplicating data blocks, it creates multiple pointer references to the same data location in the storage map. Different snapshots and clones can reference the same physical data blocks through different logical paths in the map, ensuring data integrity while avoiding actual data duplication.
Solution Approach 2:
The patent replaces the mechanical process of physically copying data blocks with a logical pointer-based reference system. Instead of moving and duplicating actual data, the system uses metadata pointers and map entries to create references, significantly reducing storage capacity consumption while maintaining data integrity.
3Productivity
If traditional storage translation methods are used, then implementation is straightforward, but performance is poor due to lack of optimization for dense and sparse information
Solution Approach 1:
The patent implements local quality by using different data organization strategies for different regions of the storage map. Dense data regions use one optimization strategy while sparse regions use another, allowing the system to optimize performance locally for each type of data distribution pattern rather than using a single approach for all data.
Solution Approach 2:
The storage map structure is designed to be dynamic, automatically adapting its organization based on the data access patterns and density. The system can dynamically adjust how data is organized and accessed in the map, optimizing performance for varying workloads without requiring manual reconfiguration.
Data Source
AI summary
A translation system can translate a request having multiple fields to a physical address using the fields as indexes to a multi-dimensional graph. A field or portion of a field can represent a location along an axis. When combined together, the fields can represent a point in n-space, where n is the number of axes. In some embodiments, a nearest neighbor calculation can be sufficient along an axis. Therefore, a point in n-space defined by the fields can be translated along an axis until a nearest neighbor entry is determined. When the entry is determined, the entry can be accessed to determine a correct response to the translation request.