Adaptive Data Compression Buffering for Throughput-Limited Transfer
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Solution Overview
Problem
Existing data transfer systems face inefficiencies in using CPU resources due to shared buffers between compression and transfer processes, leading to suboptimal throughput and network bandwidth utilization, especially when compression algorithms do not match data processing speeds.
Innovation Solution
A data transfer device that employs multiple compression algorithms, prioritizing CPU-constraint or NW-bandwidth constraint processes based on available resources and data processing speeds, with separate temporary hold parts for compressed data to optimize throughput and prevent resource wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is compressed using high-compressibility algorithms to reduce transferred data amount, then effective throughput is improved, but time variance between compression process and transfer process increases causing network bandwidth underutilization
Solution Approach 1:
The system dynamically switches between different compression algorithms (first and second algorithms) based on real-time buffer status and network conditions. When the buffer has sufficient space, high-compressibility algorithms are used; when buffer space is limited or network bandwidth is available, lower-compressibility algorithms are selected. This dynamic adaptation resolves the contradiction by adjusting compression intensity to match current system state, preventing both buffer overflow and network underutilization.
Solution Approach 2:
The system changes the compression parameter (algorithm selection) based on buffer occupancy levels and network bandwidth availability. By monitoring buffer status and adjusting the compression algorithm accordingly, the system optimizes the balance between compression ratio and processing speed, ensuring that compression keeps pace with network transfer capabilities while still achieving effective throughput improvement.
2Device complexity
If a single compression algorithm is used to simplify the system, then device complexity is reduced, but adaptability to different data types and network conditions deteriorates
Solution Approach 1:
The system implements multi-functionality by incorporating multiple compression algorithms (first compression algorithm and second compression algorithm) within a single data transfer device. Each algorithm is optimized for different scenarios - one for high compressibility when buffer space permits, and another for faster compression when buffer space is limited. This universal approach allows the system to handle various data types and network conditions effectively without requiring separate dedicated systems for each scenario.
Solution Approach 2:
The system dynamically selects which compression algorithm to apply based on real-time conditions including buffer occupancy and network bandwidth. This dynamic algorithm selection enables the system to adapt to different data types and processing speeds automatically, maintaining high adaptability while using a unified device architecture rather than multiple specialized systems.
3Speed
If compression processing speed is increased to match network transfer speed, then network bandwidth utilization is improved, but compression ratio deteriorates leading to larger data sizes
Solution Approach 1:
The system dynamically adjusts compression processing speed by selecting different algorithms based on buffer status. When buffer occupancy is high, the system uses algorithms that prioritize compression ratio over speed. When buffer occupancy is low or network transfer is fast, the system switches to algorithms that prioritize speed. This dynamic adjustment resolves the contradiction by allowing the system to optimize for compression ratio when possible and for speed when necessary, preventing both buffer overflow and excessive data sizes.
Solution Approach 2:
The system changes the compression parameter (algorithm selection) based on real-time monitoring of buffer occupancy and network conditions. By adjusting which algorithm is active, the system controls the trade-off between compression ratio and processing speed, ensuring that compressed data size remains optimized for the current network and buffer state rather than being fixed.
4Productivity
If separate temporary hold parts are used for different compression processes, then resource wastage is prevented and throughput is optimized, but device complexity increases
Solution Approach 1:
The system segments the temporary buffer into multiple distinct temporary hold parts (first temporary hold part and second temporary hold part), with each part dedicated to storing compressed data from specific compression algorithms. This segmentation prevents resource wastage by ensuring that data from different compression processes are stored separately and can be efficiently managed and transferred based on their specific characteristics and current system needs.
Solution Approach 2:
While the buffer is segmented into multiple parts, the overall temporary buffer structure serves a universal function of holding compressed data from different algorithms. The segmentation is implemented within a unified buffer management framework that can handle multiple data streams and compression types, providing specialized storage without requiring completely separate physical buffer systems for each algorithm.
Data Source
AI summary
A data transfer device compresses and transfers data according to a priority given to a CPU-constraint process imposing a constraint to a compression processing speed over a NW bandwidth-constraint process imposing a constraint to a transfer processing speed. It is necessary to select a compression algorithm, applied to the CPU-constraint process or the NW bandwidth-constraint process, based on a NW bandwidth, compressibility, and compression processing speed maximizing an effective throughput. When the amount of compressed data held in a temporary hold part is smaller than the predetermined value, the compressed data of the NW bandwidth-constraint process is stored in a temporary hold part. When the amount of compressed data held by the temporary hold part is larger than the predetermined value, the compressed data of the CPU-constraint process is stored in the temporary hold part. Thus, it is possible to improve an effective throughput by effectively using NW bandwidths.