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Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization
Susheng Zheng1, Shengxue Yang1
1Jiangxi University of Technology, Nanchang 330098, China.
Computational Intelligence and Neuroscience
|March 13, 2023
Summary
This study introduces a novel Software-Defined Networking (SDN) traffic prediction model using speech recognition for educational platforms. The model enhances network management by improving traffic distribution and avoiding packet disorder, achieving high effectiveness.
Area of Science:
- Computer Science
- Network Engineering
- Artificial Intelligence
Background:
- Software-Defined Networking (SDN) offers centralized control but faces challenges in real-time traffic management and prediction.
- Existing methods struggle with accurately labeling and evaluating the dynamic traffic situation in SDN environments.
- Educational information platforms require optimized network performance for efficient data delivery.
Purpose of the Study:
- To develop an SDN network traffic prediction model integrating speech recognition for enhanced educational information platforms.
- To analyze factors influencing SDN network traffic and establish an initial index set for traffic situation assessment.
- To propose an Intelligent Routing Strategy (IRS) mechanism for optimizing traffic flow based on stream size and network conditions.
Main Methods:
- Constructed an SDN network traffic prediction model incorporating speech recognition.
- Analyzed influencing factors of SDN equipment, links, and traffic to create an initial index set.
- Implemented a queue management algorithm in the data plane and proposed an IRS mechanism with greedy and multipath routing, and IP-based scheduling.
Main Results:
- The proposed algorithm achieved a highest effectiveness of 95.67% and a lowest Mean Squared Error (MSE) convergence of 0.0021.
- Successfully enabled quantitative evaluation of SDN network traffic situations, addressing the challenge of undetermined traffic labels.
- Demonstrated effective traffic distribution and packet disorder avoidance for large traffic streams.
Conclusions:
- The developed SDN traffic prediction model provides a new approach for global state observation in SDN network management.
- The IRS mechanism effectively optimizes traffic flow by considering stream characteristics and network resource availability.
- This research offers valuable technical support for optimizing educational information platforms through improved SDN network performance.
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