Clustered Compression History Sharing for Long-Match Network Traffic
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Solution Overview
Problem
Existing data compression methods in communication networks face inefficiencies due to short matches between input streams and compression histories, leading to poor compression ratios and increased processing overhead, especially when multiple data streams are transmitted simultaneously, and there is a need for improved synchronization and sharing of compression histories across devices.
Innovation Solution
The implementation of algorithms to identify long matches in compression histories, application-specific parsing to enhance match lengths, sharing of compression history indexes and caches across devices, and synchronization of compression histories to improve data compression efficiency and reduce bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression histories are stored on disk with long access latencies, then storage capacity is improved, but compression speed and processing efficiency deteriorate due to multiple disk accesses required for short matches
Solution Approach 1:
The compression history is segmented into multiple partitions or zones based on access patterns. Frequently accessed recent data is stored in fast memory (RAM), while less frequently accessed historical data is stored on disk. This segmentation allows the system to maintain large storage capacity while minimizing slow disk accesses by keeping hot data in fast memory.
Solution Approach 2:
The system performs preliminary actions by pre-loading compression history data into memory before it is needed for compression operations. By anticipating future compression needs and loading relevant historical data in advance, the system reduces the need for slow disk accesses during actual compression operations, thereby improving compression speed.
2Productivity
If multiple processes access compression history simultaneously, then throughput is improved, but processing overhead and access conflicts increase
Solution Approach 1:
The compression history is divided into multiple partitions that can be accessed by different processes simultaneously. Each process is assigned to specific partitions, reducing access conflicts and synchronization overhead. This segmentation enables parallel compression operations while maintaining manageable processing complexity.
Solution Approach 2:
An intermediary caching layer is introduced between multiple processes and the compression history storage. This cache acts as a buffer that can serve multiple processes simultaneously, reducing direct access conflicts to the underlying storage and lowering processing overhead associated with synchronization and access management.
3Productivity
If compression histories are shared across devices, then compression efficiency is improved, but synchronization complexity and data consistency challenges increase
Solution Approach 1:
Devices perform preliminary actions by pre-synchronizing compression histories before sharing them across the network. By establishing synchronized compression histories in advance through preliminary data exchange and validation, the system improves compression efficiency while reducing the need for frequent synchronization operations, thereby managing synchronization complexity.
Solution Approach 2:
A feedback mechanism is implemented where devices continuously monitor and report the state of their compression histories to a central coordinator or to each other. This feedback enables automatic detection of desynchronization and triggers targeted synchronization operations only when needed, improving compression efficiency while minimizing unnecessary synchronization overhead.
4Productivity
If short matches are used in compression, then processing overhead increases, but if long matches are required, then compression flexibility and applicability decrease
Solution Approach 1:
The system applies different match length requirements to different data types, applications, or compression contexts. For example, highly repetitive data may use shorter matches for efficient processing, while diverse or less repetitive data may use longer matches to maintain compression flexibility. This local quality approach allows the system to optimize processing efficiency for each specific case without sacrificing overall versatility.
Solution Approach 2:
The match length parameter is made dynamic rather than fixed. The system automatically adjusts match length based on characteristics of the input data, such as repetition patterns, data type, and compression ratio requirements. This dynamic adjustment enables the system to achieve high processing efficiency when appropriate while maintaining compression flexibility when needed, resolving the contradiction between the two objectives.
Data Source
AI summary
Systems and methods of storing previously transmitted data and using it to reduce bandwidth usage and accelerate future communications are described. By using algorithms to identify long compression history matches, a network device may improve compression efficiently and speed. A network device may also use application specific parsing to improve the length and number of compression history matches. Further, by sharing compression histories, compression history indexes and caches across multiple devices, devices can utilize data previously transmitted to other devices to compress network traffic. Any combination of the systems and methods may be used to efficiently find long matches to stored data, synchronize the storage of previously sent data, and share previously sent data among one or more other devices.