Network Data Compression Using Classification Trees
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Solution Overview
Problem
Current data compression techniques fail to achieve high compression ratios for real-time network traffic and archived data, particularly in TCP/IP networks, and require separate algorithms for online and offline compression, which is inefficient and resource-intensive.
Innovation Solution
A method involving the generation of a classification tree based on header fields to determine inter-packet and intra-packet compression plans, allowing for real-time compression of network data packets, utilizing the same algorithm for both online and offline compression, and adjusting the compression plan based on cumulative compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate compression algorithms are used for online and offline compression, then different compression needs can be addressed, but system complexity and resource consumption increase
Solution Approach 1:
The patent implements a single compression algorithm that serves both online and offline compression needs. The same compression engine processes network traffic data in real-time (online) and archived historical data (offline), eliminating the need for separate algorithm implementations. This universal approach reduces system complexity while maintaining adaptability to different compression scenarios through configurable parameters and modes within the same algorithmic framework.
2Quantity of substance
If high compression ratios are achieved for archived data, then storage requirements are reduced, but real-time compression performance may be compromised
Solution Approach 1:
The compression algorithm dynamically adjusts its processing mode based on the operational context. For online real-time compression, it optimizes for speed with lower computational overhead, accepting slightly lower compression ratios. For offline archived data compression, it switches to a more aggressive compression mode that achieves higher compression ratios. This dynamic behavior allows the same algorithm to satisfy both real-time performance requirements and storage efficiency goals without being constrained by fixed parameters.
3Productivity
If network traffic data is compressed in real-time, then bandwidth utilization is improved, but processing overhead and resource consumption increase
Solution Approach 1:
The patent applies partial compression strategies where not all data requires the same level of compression processing. The system identifies and compresses only the portions of network traffic data that benefit most from compression, leaving highly compressible or already-compressed data unchanged. This selective approach reduces unnecessary processing overhead and resource consumption while still achieving significant bandwidth utilization improvements through compression of the relevant data portions.
Data Source
AI summary
The present invention relates to a method of compressing data in a network, the data comprising a plurality of packets each having a header and a payload, the header comprising a plurality of header fields, the method comprising generating a classification tree based on at least a portion of the plurality of header fields, determining a inter-packet compression plan based on the classification tree, and performing inter-packet compression in real time for each payload of at least a first portion of the plurality of packets, the inter-packet compression being performed according to at least a portion of the inter-packet compression plan.