An Enhanced QoS-enabled Dynamic Bandwidth Allocation Mechanism for Ethernet PON

Nguyen, Tuan Dung; Eido, Thaere; Atmaca, Tülin

doi:10.1109/emerging.2009.20

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…However, when they are limited, it becomes quite challenging. It is observed that extensions from the limited algorithm [5][6][7][8][9][10] have the following problems in providing QoS and fairness, particularly when the uplink is instantaneously overloaded.…”

Section: Problem Statementmentioning

confidence: 99%

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Class-limited algorithm for supporting QoS and fairness in Ethernet passive optical network

Hwang

Yoo

2011

Photon Netw Commun

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Ethernet passive optical network (EPON) becomes a key technology for the next generation broadband access networks due to its cost-effectiveness and high data rate. One of the research issues in EPON is to support service differentiation and fairness. For service differentiation, the conventional limited algorithm is extended with strict priority queuing (SPQ). However, due to ONU-bounded bandwidth allocation, those extensions from the limited algorithm have difficulties in supporting QoS and fairness when the uplink is instantaneously overloaded. To overcome the disadvantages of ONU-bounded bandwidth allocation, instead of allocating the bandwidth on per ONU basis, the proposed class-limited algorithm allocates the bandwidth on per service class basis. Our simulations show that the class-limited algorithm is more efficient to differentiate services and to fairly share residual bandwidth than other limited schemes combined with SPQ, particularly for instantaneous overload condition.

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Section: Problem Statementmentioning

confidence: 99%

“…For fairness, the residual bandwidth is distributed proportionally to the overload. In [10], the idle period problem in the limited algorithm was studied. For better link utilization, the bandwidth allocation process is divided into two phases; normal limited operation during the transmission window and additional operation during the idle period.…”

mentioning

confidence: 99%

Class-limited algorithm for supporting QoS and fairness in Ethernet passive optical network

Hwang

Yoo

2011

Photon Netw Commun

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“…They usually present some algorithms, that use the traffic load, to dynamically adapt the bandwidth of a certain network component [1][2] [3] and improve the Quality of Service (QoS) [4]. Several works have been developed using Artificial Neural Networks (ANN) and they have shown that ANN are a competitive model, overcoming classical regression methods such as ARIMA [5][6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron and Stacked Autoencoder for Internet Traffic Prediction

Oliveira

Barbar

Soares

2014

Advanced Information Systems Engineering

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Abstract. Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management can be used to gain performance and reduce costs. The popularity of the newest deep learning methods has been increasing in several areas, but there is a lack of studies concerning time series prediction. This paper compares two different artificial neural network approaches for the Internet traffic forecast. One is a Multilayer Perceptron (MLP) and the other is a deep learning Stacked Autoencoder (SAE). It is shown herein how a simpler neural network model, such as the MLP, can work even better than a more complex model, such as the SAE, for Internet traffic prediction.

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“…Nelas são apresentados algoritmos que usam o tráfego para alterar a largura de banda de algum componente de rede [1][2] [3], melhorando a Qualidade de Serviço (QoS) [4]. Vários trabalhos foram feitos usando Redes Neurais Artificiais (RNA) e eles mostram que elas são um modelo competitivo, superando métodos clássicos de regressão como o Autoregressive Integrated Moving Average (ARIMA) [5][6] [7] [8].…”

Section: Introductionunclassified

Predição do tráfego de rede de computadores usando redes neurais tradicionais e de aprendizagem profunda

Oliveira

Barbar

Soares

2015

RITA

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Resumo: Neste artigo são comparados quatro abordagens diferentes para a previsão do tráfego de redes de computadores, usando o tráfego de dispositivos de redes de computadores que se conectam a Internet e usando Redes Neurais Artificiais (RNA) para a predição, sendo elas: (1) Multilayer Perceptron (MLP) com Backpropagation para o treinamento; (2) MLP com Resilient Backpropagation (Rprop); (3) Rede Neural Recorrente (RNN); (4) Stacked Autoencoder (SAE) com aprendizagem profunda (deep learning). Também é apresentado que um modelo de rede neural mais simples, tais como a RNN e MLP, podem ser mais eficientes do que modelos mais complexos, como o SAE. A predição do tráfego de Internet é uma tarefa importante para muitas aplicações, tais como aplicações adaptativas, controle de congestionamento, controle de admissão, detecção de anomalias e alocação de largura de banda. Além disso, mé-todos eficientes de gerenciamento de recursos, como a largura de banda, podem ser usados para melhorar o desempenho e reduzir custos, aprimorando a Qualidade de Serviço (QoS). A popularidade das novas redes neurais profundas vêm aumentado em muitas áreas, porém há uma falta de estudos em relação a predição de séries temporais, como o tráfego de Internet.

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An Enhanced QoS-enabled Dynamic Bandwidth Allocation Mechanism for Ethernet PON

Cited by 8 publications

References 9 publications

Class-limited algorithm for supporting QoS and fairness in Ethernet passive optical network

Class-limited algorithm for supporting QoS and fairness in Ethernet passive optical network

Multilayer Perceptron and Stacked Autoencoder for Internet Traffic Prediction

Predição do tráfego de rede de computadores usando redes neurais tradicionais e de aprendizagem profunda

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