Predictive Analytics for Network Traffic Management

Title

Predictive Analytics for Network Traffic Management

Creator

Jain N.; Husain S.O.; Goyal S.; Hariharasudhan S.; Victor M.; Manjula

Description

It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE.

Source

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

LSTM networks; machine learning; network traffic management; predictive analytics; real-time processing

Coverage

Jain N., Ies College of Technology, Department of Electrical & Electronics Engineering, M.P., Bhopal, India; Husain S.O., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Goyal S., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, 140307, India; Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Manjula, Saveetha Institute of Medical and Technical Sciences, Department of Mathametics, Tamilnadu, Chennai, India

Rights

Restricted Access

Relation

ISBN: 979-835039075-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IICCCS61609.2024.10763554

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214438436&doi=10.1109%2fIICCCS61609.2024.10763554&partnerID=40&md5=d44f26ed7daefa23fc7dd5486e317a68