Resource management for multimedia traffic over ATM broadband satellite networks

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A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with arXiv:1402.1128 by other author

A simulation model for video traffic performance via ATM over TCP/IP

Although TCP has emerged as the standard in data communication, the introduction of ATM technology has raised numerous problems regarding the effectiveness of using TCP over A TM networks, especially when video traffic performance is considered. This paper presents a simulation model for transmission performance of video traffic via ATM over TCP/IP. The interactivity between TCP/IP and ATM, generation of MPEG traffic and evaluation of traffic performance are implemented in the model. The design and implementation details of the model are carefully described. The experiments conducted using the model and experimental results are briefly introduced, revealing the capability of our model in simulating network events and in evaluating potential solutions to performance issues.<br /

Application of learning algorithms to traffic management in integrated services networks.

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Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future

Quality-Oriented Mobility Management for Multimedia Content Delivery to Mobile Users

The heterogeneous wireless networking environment determined by the latest developments in wireless access technologies promises a high level of communication resources for mobile computational devices. Although the communication resources provided, especially referring to bandwidth, enable multimedia streaming to mobile users, maintaining a high user perceived quality is still a challenging task. The main factors which affect quality in multimedia streaming over wireless networks are mainly the error-prone nature of the wireless channels and the user mobility. These factors determine a high level of dynamics of wireless communication resources, namely variations in throughput and packet loss as well as network availability and delays in delivering the data packets. Under these conditions maintaining a high level of quality, as perceived by the user, requires a quality oriented mobility management scheme. Consequently we propose the Smooth Adaptive Soft-Handover Algorithm, a novel quality oriented handover management scheme which unlike other similar solutions, smoothly transfer the data traffic from one network to another using multiple simultaneous connections. To estimate the capacity of each connection the novel Quality of Multimedia Streaming (QMS) metric is proposed. The QMS metric aims at offering maximum flexibility and efficiency allowing the applications to fine tune the behavior of the handover algorithm. The current simulation-based performance evaluation clearly shows the better performance of the proposed Smooth Adaptive Soft-Handover Algorithm as compared with other handover solutions. The evaluation was performed in various scenarios including multiple mobile hosts performing handover simultaneously, wireless networks with variable overlapping areas, and various network congestion levels