In this growing field of network traffic management, lower latency and real-time offloading are very important for high-quality data transmission and the productive performance of networks. The research herein presents a new approach that merges the multi-layer perceptron (MLP) with long short-term memory (LSTM) networks. The MLP is used for the beginning of feature extraction while the LSTM captures long-term dependencies and is specifically adapted for managing complex sequences of data with higher accuracy. The approach has been tailored to fit the particular dataset and problem setting leading to excellent performance metrics in relation to conventional methods. The implementation was carried out in Python using widely used libraries such as TensorFlow and Keras, which provided great flexibility and efficiency. Through empirical testing and evaluation on real-world network datasets, our most proposed model demonstrates some very promising results. More so, our hybrid MLP-LSTM model achieves accuracy up to 94%, surpassing existing offloading frameworks. The model has also displayed very high performance in terms of lowering offloading latency. The model further can generalize across various network conditions and traffic patterns such as high latency, low bandwidth, and intermittent connectivity, ensuring efficient and adaptive offloading strategies in the fledgling work scenario. These results highlight the efficiency of the hybrid MLP-LSTM approach in improving real-time network traffic management, thus paving the way for significant opportunities in applications in IoT, edge computing, and telecommunications. Supported by its implementation in Python, our model will provide a practical and easy-to-implement solution for network operators and stakeholders aiming to advance traffic offloading and combat latency problems in contemporary network infrastructure.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Jayaprakash Hampi and Vinutha C B;
Methodology: Jayaprakash Hampi;
Visualization: Jayaprakash Hampi;
Investigation: Jayaprakash Hampi and Vinutha C B;
Supervision: Vinutha C B;
Validation: Jayaprakash Hampi;
Writing- Reviewing and Editing: Jayaprakash Hampi and Vinutha C B; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Vinutha C B for this research completion and support.
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Vinutha C B
Department of Electronics and Communications Engineering, Presidency University, Bengaluru, Karnataka, India.
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Cite this article
Jayaprakash Hampi and Vinutha C B, “Development of a Hybrid MLP LSTM Model for Real Time Network Traffic Offloading and Dynamic Latency Reduction”, Journal of Machine and Computing, vol.5, no.3, pp. 1459-1476, July 2025, doi: 10.53759/7669/jmc202505116.
