Real-Time VBR Video Traffic Prediction for Dynamic Bandwidth Allocation

Liang, Yao

doi:10.1109/tsmcc.2003.818492

Cited by 56 publications

(36 citation statements)

References 42 publications

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“…The validation set is used after the neural network has been trained to assess its performance. The validation set in most cases is similar to the training set but not same [10,11].…”

Section: Estimating Traffic On Ann Predictor Modelmentioning

confidence: 99%

A Predictive Traffic Analysis on a General Purpose Heterogenous Distributed Network

Sharmi¹,

Punithavalli²,

Chauhan³

2013

IJCA

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In this paper, a new approach is proposed to predict the fractal behavior of a distributed network traffic. In this research, traffic traces are collected from a distributed network operated by NETRESEC an independent software vendor with focus on the network security field, network forensics and analysis of network traffic. A traffic analysis on packet, connection, protocol and application layers are taken into consideration. Apart from it, an investigation of self-similar and long-range dependent behavioral characteristic is made prior to the collection of traffic traces. Traffic prediction plays an important role in guaranteeing Quality of Service (QoS) in distributed networks due to the diversity of services in a realtime network application. Traffic prediction can be useful for dynamic routing, congestion control and prevention, autonomous traffic engineering, proactive management of the network etc. The forecasting methods can be broadly classified into two categories: linear prediction and nonlinear prediction models. Hence, the idea behind this research is to propose a Multiple Regression-booster equation based on the correlation structure to have a more accurate predicted traffic data result than using the later nonlinear prediction models involving Neural Networks. The traffic is sniffed and exported to NeuroSolutions builder, SPSS and then examined. Further, the exported and dissected traffic data is fed as input to train the neural network to let it predict the resultant fractal behavior of the distributed network traffic and an equation is proposed to derive the ultimate close network traffic prediction in SPSS.

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“…The validation set is used after the neural network has been trained to assess its performance. The validation set in most cases is similar to the training set but not same [10,11].…”

Section: Estimating Traffic On Ann Predictor Modelmentioning

confidence: 99%

A Predictive Traffic Analysis on a General Purpose Heterogenous Distributed Network

Sharmi¹,

Punithavalli²,

Chauhan³

2013

IJCA

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“…There are several ways to enhance the SMS ′ algorithm. For example, we may use longer lookahead windows on recorded programs to know their bit rate requirements and employ VBR video traffic models, such as [118], on live programs to predict their bit rate requirements, in order to identify spikes of the total bit rate. We may then utilize any slack time of the air medium to absorb the bit rate spikes, so that the number of playout glitches is minimized.…”

Section: Statistical Multiplexing Of Live and Recorded Video Streamsmentioning

confidence: 99%

Efficient mobile multimedia streaming

Hsu¹

2010

SIGMultimedia Rec.

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Modern mobile devices have evolved into small computers that can render multimedia streaming content anywhere and anytime. These devices can extend the viewing time of users and provide more business opportunities for service providers. Mobile devices, however, make a challenging platform for providing high-quality multimedia services. The goal of this thesis is to identify these challenges from various aspects, and propose efficient and systematic solutions to solve them. In particular, we study mobile video broadcast networks in which a base station concurrently transmits multiple video streams over a shared air medium to many mobile devices. We propose algorithms to optimize various quality-of-service metrics, including streaming quality, bandwidth efficiency, energy saving, and channel switching delay. We analytically analyze the proposed algorithms, and we evaluate them using numerical methods and simulations. In addition, we implement the algorithms in a real testbed to show their practicality and efficiency. Our analytical, simulation, and experimental results indicate that the proposed algorithms can: (i) maximize energy saving of mobile devices, (ii) maximize bandwidth efficiency of the wireless network, (iii) minimize channel switching delays on mobile devices, and (iv) efficiently support heterogeneous mobile devices. Last, we give network operators guidelines on choosing solutions suitable for their mobile broadcast networks, which allow them to provide millions of mobile users much better viewing experiences, attract more subscribers, and thus increase the revenues.

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“…With recent development of neural networks, various models are proposed and successfully applied in VBR video traffic prediction. Liang [3] proposed a three-layer feed-forward neural network based on the multi-resolution learning to predict the real-time VBR video traffic for dynamic bandwidth allocation. Chang et al [4] investigated the MPEG-1 video traffic prediction using the pipeline recurrent neural network.…”

Section: Introductionmentioning

confidence: 99%

Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network

Sun

Cui

Liu

et al. 2012

2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

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Variable bit rate (VBR) video traffic, exhibiting high self-similarity and burstiness, has been a major traffic component in high speed network. However, its complex bit rate distribution makes VBR video traffic prediction, especially multistep ahead prediction, very difficult. Recently, deterministic echo state network with adjacent-feedback loop reservoir structure (ALR) was proved to have high prediction accuracy, good memory capacity, and simple structure. In the paper, we apply ALR to real-time VBR video traffic prediction. The proposed scheme makes use of loop reservoir with identity activation function to conduct sample learning in high dimension states. Experimental results show that the simplified ALR scheme can effectively capture dynamic characteristics of VBR video traffic with less training time. Its multistep prediction accuracy is improved by 2% on average, compared with the neural network based on multi-resolution learning.

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Real-Time VBR Video Traffic Prediction for Dynamic Bandwidth Allocation

Cited by 56 publications

References 42 publications

A Predictive Traffic Analysis on a General Purpose Heterogenous Distributed Network

A Predictive Traffic Analysis on a General Purpose Heterogenous Distributed Network

Efficient mobile multimedia streaming

Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network

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