Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks

Sisay Tadesse Arzo, Zeinab Akhavan, Mona Esmaeili, Michael Devetsikiotis and Fabrizio Granelli
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Sisay Tadesse Arzo: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
Zeinab Akhavan: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
Mona Esmaeili: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
Michael Devetsikiotis: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
Fabrizio Granelli: Department of Information Engineering and Computer Science (DISI), University of Trento, 38123 Trento, Italy

Future Internet, 2022, vol. 14, issue 8, 1-23

Abstract: Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.

Keywords: network management automation; multi-agent system; network traffic classifier; network traffic predictor; machine learning; deep learning; 5G/6G; future network architecture; service-oriented architecture (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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