Traffic Matrix Prediction Based on Deep Learning for Dynamic Traffic Engineering

Liu, Zhifeng; Wang, Zhiliang; Yin, Xia; Shi, Xingang; Guo, Yingya; Tian, Ying

doi:10.1109/iscc47284.2019.8969631

Cited by 30 publications

(8 citation statements)

References 21 publications

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“…It is this approach that was used in [9]. Several methods were proposed for predicting the traffic matrix (TM) based on neural networks (NN) and predicting the traffic matrix from three points of view: directly predicting the total TM, predicting each origin-destination (OD) flow separately and predicting the overall TM combined with the correction of key elements.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of the NARX neural network for predicting a one-dimensional time series

Serikov

Zhetpisbayeva

Mirzakulova

et al. 2021

EEJET

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Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of the NARX neural network for predicting a one-dimensional time series

Serikov

Zhetpisbayeva

Mirzakulova

et al. 2021

EEJET

View full text Add to dashboard Cite

show abstract

“…To accomplish query prediction, we can leverage deep recursive neural networks (RNNs) to conduct time-series forecasting. Indeed, a significant amount of work has gone into improving time-series prediction, and the technology is now used in any number of applications, including predicting stock prices [50], [51], network traffic [52], [53], electricity load in power grids [54]- [56], and more.…”

Section: ) Query Predictionmentioning

confidence: 99%

Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

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“…In view of the time characteristics of traffic, RNN and its variants are frequently used in traffic forecasting. Liu et al [25] proposed several TM prediction methods based on Neural Networks (NN) and predict TM from three perspectives: predict the overall TM directly, predict each origin-destination (OD) flow separately and predict the overall TM combined with key element correction. The experiment results show that prediction methods based on Recurrent Neural Networks (RNN) can achieve better prediction accuracy than methods leveraging Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN).…”

Section: Related Workmentioning

confidence: 99%

Traffic Anomaly Detection Method Based on Improved GRU and EFMS-Kmeans Clustering

Huo¹,

Cao²,

Wang³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

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In recent years, with the continuous development of information technology and the rapid growth of network scale, network monitoring and management become more and more important. Network traffic is an important part of network state. In order to ensure the normal operation of the network, improve the availability of the network, find network faults in time and deal with network attacks; it is necessary to detect the abnormal traffic in the network. Abnormal traffic detection is of great significance in the actual network management. Therefore, in order to improve the accuracy and efficiency of network traffic anomaly detection, this paper proposes a comprehensive anomaly detection method based on improved GRU traffic prediction and improved K-means clustering, and cascade the traffic prediction and clustering to achieve the purpose of anomaly detection. Firstly, an improved highway-GRU algorithm HS-GRU (An improved Gate Recurrent Unit neural network based on Highway network and STL algorithm, HS-GRU) is proposed, which combines STL decomposition algorithm with highway GRU neural network and uses this improved algorithm to predict traffic. And then, we proposed the EFMS-Kmeans algorithm (An improved clustering algorithm that combined Mean Shift algorithm based on electrostatic force with K-means clustering) to solve the shortcoming of the traditional K-means clustering which cannot automatically determine the number of clustering. The sum of the squared errors (SSE) method and the contour coefficient method were used to double test the clustering effect. After determining the clustering center, the potential energy gradient was directly used for anomaly detection by using the threshold method, which considered the local characteristics of the data and ensured the accuracy of anomaly detection. The simulation results show that the anomaly detection algorithm based on HS-GRU and EFMS-Kmeans clustering proposed in this paper can effectively improve the accuracy of flow anomaly detection and has important application value.

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Traffic Matrix Prediction Based on Deep Learning for Dynamic Traffic Engineering

Cited by 30 publications

References 21 publications

Application of the NARX neural network for predicting a one-dimensional time series

Application of the NARX neural network for predicting a one-dimensional time series

Towards Efficient and Intelligent Internet of Things Search Engine

Traffic Anomaly Detection Method Based on Improved GRU and EFMS-Kmeans Clustering

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