Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion

Shi, Jinmei; Leau, Yu‐Beng; Li, Kun; Park, Yong‐Jin; Zhou, Yan

doi:10.1109/access.2020.3036421

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…Then, for the sub-data set corresponding to each mode after decomposition, the predictive sub-models are established respectively using the integrated extreme learning machine. Finally, the outputs of all the sub-models are integrated to get the outcome [37] . In the process of designing a prediction model, several optimized parameters are involved in VMD, PSR and ELM.…”

Section: Discussionmentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

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Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.

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Section: Discussionmentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

Self Cite

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“…In this section, a brief introduction for the different types of prediction models is given, including statistical, machine learning and deep learning models. More comprehensive discussions can be found in recent relevant surveys [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Internet Traffic Prediction with Distributed Multi-Agent Learning

Jiang

He²,

Gu³

2022

ASI

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Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches are trained and deployed in a centralized approach, without considering the realistic scenario in which multiple parties are concerned about the prediction process and the prediction model can be trained in a distributed approach. In this study, a distributed multi-agent learning framework is proposed to fill the research gap and predict Internet traffic in a distributed approach, in which each agent trains a base prediction model and the individual models are further aggregated with the cooperative interaction process. In the numerical experiments, two sophisticated deep learning models are chosen as the base prediction model, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). The numerical experiments demonstrate that the GRU model trained with five agents achieves state-of-the-art performance on a real-world Internet traffic dataset collected in a campus backbone network in terms of root mean square error (RMSE) and mean absolute error (MAE).

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“…Some review studies also comment on the problem of network traffic prediction. In the literature, 36 the authors discussed past network traffic prediction research, critically examined the optimization and decomposition technologies used in the model, and listed the model parameter structure based on the research methodology, the data set used, the evaluation criteria, and so on. By comparison, digging out the particle swarm optimization (PSO) algorithm and the VMD decomposition technique will effectively solve the network traffic model predictive difficulties that have proven to be crucial to improving predictive accuracy and convergence speed strategy.…”

Section: Introductionmentioning

confidence: 99%

A novel network traffic combination prediction model

Tian

Song

2022

Int J Communication

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Summary Network has become an indispensable part of public life. To improve network utilization, network performance, network quality, and enhance network security, precise prediction of network traffic is an indispensable method and basis for solving the above problems. In order to accurately predict the network traffic, a novel combination prediction model for network traffic is proposed. In this model, local mean decomposition (LMD), bidirectional long short‐term memory (BiLSTM), and Bayesian optimization algorithm are combined. First, the LMD method decomposes the network traffic time series to obtain several product function (PF) components and a residual by LMD. Then, each PF component and residual is predicted with BiLSTM model. Meanwhile, the Bayesian optimization algorithm is introduced to optimize the hyperparameters of BiLSTM. Finally, the predicted value of each PF component and residual is linearly superimposed to obtain the final predicted value. Through the study of two groups of actual network traffic datasets and compared with a variety of state‐of‐the‐art prediction models, the proposed model has a preferable prediction results by comparison of the results.

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Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion

Cited by 13 publications

References 38 publications

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Internet Traffic Prediction with Distributed Multi-Agent Learning

A novel network traffic combination prediction model

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