Adaptive Compression Management for Network Data Transmission
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Solution Overview
Problem
Current data transmission techniques, such as compression methods, often result in increased processing time for compression and decompression, which can exceed the time saved by reducing file size, leading to decreased network performance, especially when dealing with large image data files.
Innovation Solution
A technique for automatically managing compression levels based on monitoring server, client, and network parameters, as well as user preferences and priority levels, to determine optimal compression levels for each client device, minimizing the cumulative difference between actual and expected data transfer performance or image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If compression techniques are used to reduce file size, then network transmission time is reduced, but compression and decompression processing time increases
Solution Approach 1:
The system dynamically adjusts compression levels based on real-time monitoring of server characteristics, client characteristics, and network parameters. Different compression levels are applied to different clients and at different times, making the compression approach adaptive rather than static, thereby optimizing the balance between transmission time and processing overhead.
Solution Approach 2:
The invention changes the compression parameter (compression level) as a variable that can be adjusted based on system conditions. By monitoring characteristics and parameters of servers, clients, and networks, the system selects optimal compression levels to minimize total time (compression + transmission + decompression), resolving the contradiction between reduced file size and increased processing time.
2Quantity of substance
If centralized storage is used to reduce resource requirements, then storage efficiency improves, but data transmission time increases due to network bandwidth limitations
Solution Approach 1:
The system applies different compression qualities to different clients based on their specific characteristics and requirements. High-priority or time-sensitive clients may receive data with lower compression (faster decompression), while other clients receive higher compression levels, optimizing the balance between centralized storage efficiency and transmission speed for each recipient.
Solution Approach 2:
The system pre-monitors and evaluates server, client, and network parameters to determine optimal compression levels before data transmission begins. This preliminary assessment allows the system to prepare appropriate compression settings in advance, reducing the actual transmission time by avoiding suboptimal compression choices.
3Loss of energy
If high compression levels are applied to reduce network traffic, then bandwidth utilization improves, but image quality deteriorates
Solution Approach 1:
The system varies the compression parameter based on monitored characteristics and client requirements, selecting compression levels that achieve acceptable bandwidth utilization while maintaining sufficient image quality for each specific client scenario.
Solution Approach 2:
The system monitors actual versus expected data transfer performance and image quality metrics, using this feedback to adjust compression levels dynamically. This closed-loop approach ensures that compression does not excessively degrade image quality while still achieving bandwidth reduction goals.
Data Source
AI summary
A method for managing data transmission between computing devices is disclosed. In one embodiment, the method includes monitoring a plurality of parameters of a computer network that includes a server and a client. The plurality of parameters may include a client resource parameter, a server resource parameter, and a network operating parameter. The disclosed method may also include automatically determining a desired compression level at which to send data to the client based at least in part on the client resource parameter, the server resource parameter, and the network operating parameter. Further, in one embodiment the method may include communicating the data from the server to the client at the desired compression level in response to a client request for the data. Various other methods, systems, and manufactures are also disclosed.