Source Node Packet Compression with Dynamic Function Selection
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Solution Overview
Problem
Current data compression methods in networking systems, particularly hardware compression, face challenges in efficiently managing network traffic and bandwidth due to limitations in identifying optimal compression functions for varying network traffic types and destination nodes, leading to suboptimal packet size reduction and increased processing time.
Innovation Solution
The implementation of a source node apparatus that includes a data analyzer, policy determiner, learning machine, compression engine, and source modifier to identify and apply appropriate hardware compression functions based on destination node policies and traffic types, modifying protocol identifiers to indicate compression methods used, thereby optimizing packet size reduction and network bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If hardware compression is used to compress data, then network traffic is reduced, but the ability to efficiently manage varying network traffic types and destination nodes is limited
Solution Approach 1:
The system dynamically selects compression functions based on real-time analysis of destination node characteristics and traffic types. The source node apparatus continuously adapts its compression strategy by identifying optimal compression functions from a plurality of available functions, transforming the static hardware compression approach into a dynamic, context-aware system that resolves the contradiction between compression efficiency and adaptability.
Solution Approach 2:
The system changes operational parameters by selecting different compression functions based on varying conditions such as destination node policies and traffic types. Each compression function represents a different parameter configuration, allowing the system to optimize compression performance for specific traffic patterns and destination characteristics, thereby achieving both reduced traffic volume and high adaptability.
2Quantity of substance
If multiple compression functions are evaluated to find the optimal one, then compression efficiency improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of destination node characteristics and traffic types before selecting a compression function. By pre-identifying the optimal compression function based on known parameters, the system avoids time-consuming trial-and-error compression attempts, thus achieving both efficient packet size reduction and minimized processing time.
Solution Approach 2:
The system uses feedback from packet transmission results to refine future compression function selections. By analyzing the effectiveness of previously used compression functions and adjusting selections based on this feedback, the system learns to make faster, more accurate choices, reducing both processing time and packet size over time through iterative optimization.
Data Source
AI summary
An apparatus is disclosed to compress packets, the apparatus comprising; a data analyzer to identify a new destination address and a protocol identifier of an input packet corresponding to a new destination node and a communication system between the new destination node and a source node; a compression engine to utilize a plurality of compression functions based on the new destination address and the protocol identifier and reduce a size of the input packet; a compression analyzer to identify a reduced packet and a compression function identifier corresponding to the reduced packet, the compression function identifier associated with one of the compression functions; and a source modifier to construct a packet to include the compression function identifier by modifying unregistered values of a protocol identifier by a difference associated with the compression function identifier, the packet to inform the new destination node of a compression function.