Comparing forecasting approaches for Internet traffic

Katris, Christos; Daskalaki, Sophia

doi:10.1016/j.eswa.2015.06.029

Cited by 67 publications

(38 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness of these auto-encoder ANNs have been demonstrated in temperature prediction [28], weather forecasting [29], prediction of traffic flows [30] or prediction of power consumption in a data centre [31]. The comparison presented in [13] shows that FARIMA processes and ANNs have similar approximation errors. However, the best results are achieved for hybrid solutions which employ both FARIMA and ANNs simultaneously [13,32,33].…”

Section: Related Workmentioning

confidence: 96%

“…The comparison presented in [13] shows that FARIMA processes and ANNs have similar approximation errors. However, the best results are achieved for hybrid solutions which employ both FARIMA and ANNs simultaneously [13,32,33].…”

Section: Related Workmentioning

confidence: 99%

“…They can capture the linear and short range dependencies occurring in time series, but when used for general Internet traffic which exhibits LRD, the ARIMA processes have rather poor performance. Nevertheless, due to their versatility and relative easiness of use, they are extensively applied for modelling and forecasting of network traffic, e.g., [13,46]. In order to capture LRD of network traffic and obtain better accuracy of a model, some research use FARIMA processes [47,48].…”

Section: Arima and Farima Processesmentioning

confidence: 99%

See 2 more Smart Citations

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

View full text Add to dashboard Cite

During the past years, adaptive video based on HTTP has become very popular. Streaming of the adaptive video relies heavily on an estimation of end-to-end network throughput, which can be challenging especially in mobile networks, where the capacity highly fluctuates. In this work, we propose to predict the network throughput using its past measurements. As the analysis shows, the network throughput forms a long range-dependent process; thus, for the throughput prediction, we apply a fractional ARIMA process and artificial neural networks. Our approach does not require any modifications to the network infrastructure or the TCP stack. The predictions are performed for data traces obtained from measurements of throughput of a real mobile network. As the experiment shows, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm. Compared to the throughput predictors employed in contemporary systems dedicated to adaptive video streaming, the proposed technique obtains better results when taking into account effectiveness of network capacity utilisation and stability of video play-out.

show abstract

Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Section: Arima and Farima Processesmentioning

confidence: 99%

See 1 more Smart Citation

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

View full text Add to dashboard Cite

show abstract

“…However, unlike linear ARIMA and FARIMA, ANNs can handle also non-linear phenomena in time series [26,27]. Thus, ANNs have been also widely applied for network traffic modelling and prediction [28,29].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

View full text Add to dashboard Cite

The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

show abstract

“…This implies different processing steps: management of a track record with the data required to build forecasting models, analysis of the data characteristics which are relevant for deciding the best suited prediction algorithms, construction of forecasting models, decision of prediction algorithms, forecasting and evaluation of the results in order to learn from the previous decisions. Note that as stated in [47], the prediction of network events enhances the optimization of resources, allows the deployment of proactive actions and anticipates risk identification. The SELFNET Analyzer focuses primarily on infer predictions from two data structures: time series and graphs.…”

Section: Analyzer Module Architecturementioning

confidence: 99%

An Approach to Data Analysis in 5G Networks

López

Vidal

Villalba

2017

Entropy

View full text Add to dashboard Cite

5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.

show abstract

Comparing forecasting approaches for Internet traffic

Cited by 67 publications

References 31 publications

Traffic prediction methods for quality improvement of adaptive video

Traffic prediction methods for quality improvement of adaptive video

Analysis and modelling of traffic produced by adaptive HTTP-based video

An Approach to Data Analysis in 5G Networks

Contact Info

Product

Resources

About