Incremental Block Compression for Storage Efficiency
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Solution Overview
Problem
Existing data compression techniques, such as block compression, are computationally expensive and inefficient, leading to increased processor workload and potential performance degradation, especially when incrementally compressing data to reach a specific block size target in storage systems.
Innovation Solution
Implementing a compression processor that incrementally compresses data in portions until reaching a target block size, using padding if necessary, and managing metadata to optimize storage efficiency while reducing processor workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If block compression is used to compress data, then storage efficiency is improved, but computational cost and processor workload increase significantly
Solution Approach 1:
The patent divides the data compression process into incremental blocks, where data is compressed in smaller portions rather than processing the entire data stream at once. This segmentation allows the system to achieve compression efficiency while distributing the computational load over time, preventing processor overload and maintaining system performance.
Solution Approach 2:
The system performs preliminary compression actions by incrementally compressing data as it becomes available, rather than waiting for complete data blocks. This preliminary processing approach reduces the total computational burden by performing compression in smaller, more manageable increments, thereby reducing overall processor workload while maintaining storage efficiency.
2Quantity of substance
If incremental compression is used to reach target block size, then storage efficiency is improved, but system performance may decrease due to frequent compression operations
Solution Approach 1:
The patent implements dynamic compression block sizing, where the system adjusts the compression block size based on available data and target storage requirements. This dynamic approach allows the system to optimize between compression efficiency and performance by adapting block sizes to current operational conditions, preventing excessive frequent operations while maintaining storage efficiency.
Solution Approach 2:
The system changes compression parameters such as block size, compression level, and processing rate based on real-time conditions. By adjusting these parameters dynamically, the system can maintain high storage efficiency while preventing performance degradation, as the compression operations are tuned to match current system capabilities and data characteristics.
Data Source
AI summary
Techniques for performing incremental block compression using a processor are described herein. The processor receives a request to compress input data, the request including compression parameters for the compression and a target block size. The processor divides the input data into portions. The processor iteratively compresses the input data to an output block, until compressing another portion of data would increase a file size of the output block over a threshold value that is based at least on the target block size.