Bitmap Storage Management Divisible Block Allocation
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Solution Overview
Problem
Conventional bitmap-based storage space management systems face inefficiencies when handling large and small data objects, as they either waste space with large blocks for small data or decrease efficiency with small blocks for large data, leading to suboptimal storage and access performance.
Innovation Solution
A two-layered bitmap-based system with index bitmaps (main resource, large block, sub-resource, and small block) and state bitmaps (resource and distribution) that dynamically manage storage blocks as unoccupied, occupied divisible, or occupied indivisible, allowing for efficient distribution and release of large and small blocks by modifying state bits accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed block size is used in conventional bitmap-based storage management, then the system is simple to implement, but storage efficiency decreases when storing large data objects or small data objects respectively
Solution Approach 1:
The patent divides storage blocks into multiple sizes (small blocks and large blocks) and uses separate bitmaps to manage each block size. This segmentation allows the system to allocate appropriately-sized blocks to different data objects, improving storage efficiency while maintaining implementation simplicity through modular bitmap management.
Solution Approach 2:
The system dynamically selects block sizes based on data object requirements. The bitmap structure supports flexible allocation where small blocks are used for small data objects and large blocks are used for large data objects, making the storage management adaptive rather than fixed.
2Productivity
If large blocks are distributed for small data objects, then storage space is allocated quickly, but a lot of space is wasted
Solution Approach 1:
The patent applies different block sizes to different storage locations based on local requirements. Small blocks are allocated for small data objects to minimize waste, while large blocks are allocated for large data objects to ensure sufficient space. The separate bitmaps for small and large blocks enable this localized optimization.
3Loss of energy
If small blocks are distributed for large data objects, then storage space is utilized efficiently, but access efficiency decreases
Solution Approach 1:
The system applies the appropriate block size to each storage location based on the data object size. Large blocks are provided to large data objects to enable efficient access, while small blocks are used for small data objects to maximize storage utilization. This localized matching of block size to data size resolves the contradiction between storage efficiency and access efficiency.
Data Source
AI summary
A bitmap-based storage space management system and methods thereof are provided, which realize divisible storage blocks that a small block is distributed when storing a small data object and a large block is distributed when storing a large data object. Through a two layer structure of index bitmaps and state bitmaps, a management of a storage space is realized. The index bitmaps comprise a main resource index bitmap (11), a large block index bitmap (12), a sub-resource index bitmap (13) and a small block index bitmap (14). The state bitmaps comprise a resource state bitmap (21) and a distribution state bitmap (22). Through the resource and distribution state bitmaps, the storage space is described respectively as an unoccupied divisible state, an occupied divisible state and an occupied indivisible state. In an above storage space structure, large block distribution, small block distribution, large block release and small block release are realized.