Data-Dependent Chunking for Low-Bandwidth Stream Compression
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Solution Overview
Problem
Existing data streaming methods face inefficiencies in bandwidth utilization and compression, particularly in low-bandwidth networks, where traditional compression techniques are restrictive and computationally costly, leading to delays in data transfer and increased memory overhead.
Innovation Solution
The method involves partitioning a byte code sequence into data chunks with unique signatures, comparing these signatures to a history buffer for matching, and transmitting references to similar chunks, allowing for tunable chunk sizes and memory overhead, and using secondary compression to enhance efficiency, particularly suitable for real-time applications like RDP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional compression techniques are used on data streams, then compression is achieved, but computational cost increases and delays occur
Solution Approach 1:
The data stream is divided into fixed-size chunks before compression. Each chunk is processed independently through the compression algorithm, allowing parallel processing and reducing the computational burden on any single chunk. This segmentation enables the system to achieve compression ratios similar to full-stream compression while significantly reducing the time delay, as each chunk can be compressed and transmitted independently without waiting for the entire data stream.
2Reliability
If data is transmitted as a whole, then complete data transfer is achieved, but bandwidth utilization is inefficient
Solution Approach 1:
The complete data stream is segmented into smaller fixed-size chunks that are transmitted independently over the network. This segmentation allows for more efficient bandwidth utilization as each chunk can be transmitted as soon as it is ready, rather than waiting for the entire data set to be prepared. The receiver reconstructs the complete data by reassembling the chunks in order, maintaining data transfer completeness while improving bandwidth efficiency through pipelined transmission.
3Loss of substance
If chunk-based compression is used, then bandwidth usage is reduced, but memory overhead increases
Solution Approach 1:
The system allows dynamic adjustment of the chunk size parameter to optimize the balance between bandwidth usage and memory overhead. By changing the chunk size parameter, users can trade off between transmitting more frequently (smaller chunks, lower bandwidth usage per transmission but higher memory overhead for managing more chunks) or transmitting less frequently (larger chunks, higher bandwidth usage per transmission but lower memory overhead). This parameter adjustment capability enables the system to adapt to different network conditions and memory constraints.
Data Source
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AI summary
Systems and methods for compression of data to be transmitted are described. A data stream, such as a byte code sequence, is partitioned into one or more data chunks. The data chunks can be associated with an identifier, such as a signature that differentiates one data chunk from the other. Thus, different data chunks would be identifiable through different signatures. The data chunks associated with the signatures can be compared with a repository or a history buffer. The history buffer includes a collection of previously transmitted data chunks each associated with their respective signature.