Variable bit rate video traffic prediction based on kernel least mean square method

Haghighat, Nasser; Kalbkhani, Hashem; Shayesteh, Mahrokh G.; Nouri, Mehdi

doi:10.1049/iet-ipr.2014.1035

Cited by 13 publications

(6 citation statements)

References 43 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More detail traffic characteristics and coding parameters of the H.264 video traces can be found in [21]. In our experiment, the used live real-world H.264 video traffic traces include the 3D movie, sport and news, covering a wide range of popular video samples, as listed in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Sun¹,

Li²,

Zhang³

2017

IJMUE

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Sun¹,

Li²,

Zhang³

2017

IJMUE

View full text Add to dashboard Cite

show abstract

“…For example, it has been used in proactive resource management [15], mobile data offloading [16], data-center traffic management [17], optimizing inter-data-center traffic flows [18], and forecasting big-data applications demands [19]. State-of-the-art traffic predictors are based on different machine learning algorithms including neural networks [1], Wavelet transform [18], kernel-based methods [2], timeseries analysis [3], and LASSO [20]. ARIMA is a class of statistical models for analyzing and forecasting time-series data that has been used for SRD traffic modelling and prediction [21].…”

Section: Related Workmentioning

confidence: 99%

“…Traffic prediction is challenging because the behaviour of network traffic is affected by many factors including users' behaviour, network protocols, topology, and management policies. Many predictive models have been proposed based on different algorithms including neural networks [1], kernel-based methods [2], time-series models [3], etc. They rely mainly on a single learner to train and forecast the traffic data.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression Ensemble Model for Network Traffic Prediction

2020

View full text Add to dashboard Cite

Network traffic prediction is substantial for network optimization and resource management. However, designing an efficient predictive model considering different traffic characteristics, including periodicity, nonlinearity, and nonstationarity, is challenging. Recently, ensemble learning is attracting much attention from researchers in the machine learning community. Although ensemble learning has proven exceptional performance in modelling the intricate problems, it may not be able to handle varying patterns and chaotic behaviour, which are typical properties of traffic data (and many other time-series problems). For this reason, ensemble methods show a limited prediction accuracy in network traffic modelling. We address this issue by proposing an ensemble of learners for time-series prediction that considers the accuracy of individual learners as well as diversity among their outcomes. Each learner contributes to the optimization process by finding the optimal accuracy-diversity balance in a segment of feature space. This divideand-conquer approach avoids complicated objective functions with many local optimums while fitting the ensemble model on large datasets. Experimental results on the real traffic traces show our proposed method outperforms other state-of-the-art predictors with 12% on average in prediction accuracy for different datasets.

show abstract

“…The key issue is how to improve nonlinear approximation capacity as much as possible [23]- [25]. It is demonstrated that classic AR, ARMA and ARIMA can only seizure both linearity and short range dependencies (SRD) hidden between video data, but are powerless to LRD, which leads to weak performance in video traffic prediction [26]. In [27], a logistic smooth transition autoregressive was developed for VBR video traffic prediction, where adaptive least mean square and its extensions were considered for the determination of model parameters.…”

Section: Introductionmentioning

confidence: 99%

Smoothing-Aided Support Vector Machine Based Nonstationary Video Traffic Prediction Towards B5G Networks

Wang

Sun

et al. 2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.

show abstract

Variable bit rate video traffic prediction based on kernel least mean square method

Cited by 13 publications

References 43 publications

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Gaussian Process Regression Ensemble Model for Network Traffic Prediction

Smoothing-Aided Support Vector Machine Based Nonstationary Video Traffic Prediction Towards B5G Networks

Contact Info

Product

Resources

About