An Empirical Study of Throughput Prediction in Mobile Data Networks

Liu, Yan; Lee, Jack Y. B.

doi:10.1109/glocom.2014.7417858

Cited by 13 publications

(13 citation statements)

References 14 publications

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“…Traffic & Throughput [5] uses trace-driven simulation to compare prediction errors obtained using different techniques. [6] uses real network traffic to evaluate prediction techniques and to discuss their practical challenges.…”

Section: Topicmentioning

confidence: 99%

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

220

137

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A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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Section: Topicmentioning

confidence: 99%

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

220

137

View full text Add to dashboard Cite

show abstract

“…At the beginning, the majority of the works was measurement-based, focusing on the characterisation of various aspects of HTTP video and its usage patterns. On the one hand, there are publications based on crawling web video sites for an extended period of time [33][34][35]. These works, which examined video popularity and users' behaviour, showed that statistics such as length, access patterns, growth trend, and active life span were quite different in comparison to traditional video streaming applications.…”

Section: Previous Workmentioning

confidence: 99%

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

Biernacki

2016

Multimed Tools Appl

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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.

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“…Generally, there are no conclusive guidelines regarding the process of network traffic prediction with ANNs. Some authors state that the performance of traffic prediction model can be improved by multiplying the number of layers instead of increasing the number of neurons [25], while other research reveals that more complex algorithms are not necessarily better, and there exists a specific range of operating parameters where predictions are generally more accurate [26]. Nevertheless, so-called deep ANNs have become a popular tool employed in machine intelligence.…”

Section: Related Workmentioning

confidence: 99%

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

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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.

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An Empirical Study of Throughput Prediction in Mobile Data Networks

Cited by 13 publications

References 14 publications

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

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

Traffic prediction methods for quality improvement of adaptive video

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