Context-Based Traffic Compression for Star Network Hubs
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Solution Overview
Problem
In communications networks with a star topology, the routing capacity of the central hub becomes a limiting factor due to the need for all network traffic to traverse it, and existing data compression techniques do not efficiently address this issue, especially in networks with large numbers of nodes.
Innovation Solution
The method involves computing an optimal dictionary for each context of data traffic within the network, identifying block contexts, and encoding data using the respective dictionary to optimize compression, which reduces the number of dictionaries needed and enhances compression efficiency by leveraging context-specific characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If all network traffic traverses the central hub in a star topology network, then the network can be simply structured and easily managed, but the routing capacity of the hub becomes a limiting factor on overall network performance
Solution Approach 1:
The patent segments the network traffic into different contexts and applies different compression dictionaries to each context. This segmentation allows the system to handle diverse traffic types efficiently without overwhelming the hub's routing capacity, as compressed data requires less bandwidth and processing.
Solution Approach 2:
The patent changes the parameter of data representation by applying context-specific compression dictionaries. By transforming data into compressed form before transmission, the effective bandwidth and routing capacity of the hub are enhanced, allowing more traffic to pass through without increasing physical infrastructure.
2Productivity
If general data compression techniques are applied to network traffic, then some compression is achieved, but compression efficiency is insufficient for context-specific data patterns
Solution Approach 1:
The patent applies local quality by using different compression dictionaries for different contexts of data traffic. Each context (e.g., file transfers, web browsing, email) receives a dictionary optimized for its specific patterns, achieving high compression efficiency for each type without requiring a single complex universal compressor.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing compression dictionaries for various contexts before actual data compression occurs. This allows the compression process itself to be simple and fast, as the complex dictionary creation work has already been done in advance.
3Productivity
If multiple compression dictionaries are maintained for different data types, then compression efficiency for specific data types improves, but the number of dictionaries required increases system complexity
Solution Approach 1:
The patent changes the parameter of dictionary selection by using context identification as a key. Instead of maintaining separate dictionaries for every possible data type, the system identifies the context of incoming data and selects from a manageable set of context-specific dictionaries, reducing the total number needed while maintaining high efficiency.
Data Source
AI summary
A method of compressing data traffic for transmission through a network, the method comprises computing, for each one of a plurality of contexts of data traffic within the network, a respective optimal dictionary for encoding data traffic; For each block of data to be transmitted through the network: identifying a block context of the block of data; and encoding the block of data using the respective dictionary computed for the context corresponding to the identified block context.