Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

The Internet Protocol.

Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability Multi-core platforms significantly promote SDRs’ parallel computing capacities, enabling them to adopt artificial intelligent techniques, ie, deep learning, to manage routing paths In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead

Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning

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 Traffic Matrix (TM) in large networks. By validating our framework on real-world data from G ´ EANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.

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

This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs). TM prediction is defined as the problem of estimating future network traffic matrix 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 data and classify or predict time series with time lags of unknown size. LSTMs have been shown to model longrange dependencies more accurately than conventional RNNs. NeuTM is a LSTM RNN-based framework for predicting TM in large networks. By validating our framework on real-world data from GEANT network, we show that our model converges quickly and gives state of the art TM prediction performance.

NeuTM: A neural network-based framework for traffic matrix prediction in SDN

Whatever the Network of the Future turns out to be, there is little doubt that QoS will constitute a fundamental requirement. However, QoS issues and the respective solutions will not remain unchanged. New challenges will be raised; new ways of dealing with QoS will be enabled by novel networking concepts and techniques. Thus, a fresh approach at the QoS problem will be required. This paper addresses QoS in a Future Internet scenario and is focused on three emerging concepts: Network Virtualization, enabling the coexistence of multiple network architectures over a common infrastructure; In-Network Management, improving scalability of management operations by distributing management logic across all nodes; the Generic Path based on the semantic resource management concept, enabling the design of new data transport mechanisms and supporting different types of communications in highly mobile and dynamic network scenarios.

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Quality of service in the Future Internet

The paper is focused on Nagios, an open-source flexible monitoring tool for enterprises. The goal was to add new objects (i.e. QoS parameters) in a management information base MIB starting with the root object identifier OID .1.3.6.1.4.1.8072.9999.9999.1. Furthermore, we wanted to prove that the integration of this enhanced software tool running on several platforms (Android, Windows7, Fedora Core) is a benefit for network administrators. The newly created SNMP Agent and with the existing Nagios Agent were able to update the parameters: Available Transfer Rate ATR and One-Way-Delay OWD. Due to cross-layer techniques involved, the overall solution is an evolutionary step forward towards Future Internet implementations (although it is still stick on SNMP approach).

Nagios-based network management for Android, Windows and Fedora Core terminals using Net-SNMP agents

In this paper a distributed routing management solution is described that takes into consideration statistical Quality of Service (QoS) information about the state of network links. The goal is to offer dynamic metrics to the routing algorithm based on the current resource utilization, i.e. the routing metrics will depend on the traffic load and the delay measured on individual links in real-time. Identification of congested areas allows situation-aware path selection, enhancing the global capacity of the network by determining optimal routes. Streszczenie. W artykule przedstawiono sposob zarządzania routowaniem, w ktorym wzieto pod uwage parameter QoS na temat stanu linkow w sieci. Glownym celem bylo zastosowanie dynamicznej metryki do algorytmu routowania, opartej na informacji o biezącym wykorzystaniu zasobow. Identyfikacja rejonow przeciązenia pozwala na polepszenie dzialania i pojemności sieci poprzez optymalizacje ściezki routowania. (Kontrola przeciązenia w routowaniu QoS).

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Congestion Control Based on Distributed Statistical QoS-Aware Routing Management

The paper proposes a congestion control protocol based on Network Coding (NC) operations for the butterfly topology. The proposed protocol defines XOR-based coding and decoding algorithms adapted to streams having different rates and characteristics, as well as a signalling protocol required for dynamic activation and deactivation of NC operations. Some general principles related to the integration of NC operations into data transmission protocols are also discussed. The congestion control protocol proposed was simulated in OMNeT++ and implemented in a real network to demonstrate its feasibility and to test the functioning of the proposed coding and signalling algorithm in different scenarios.

Andrei Bogdan Rus

Papers

Quality of service in the Future Internet

Nagios-based network management for Android, Windows and Fedora Core terminals using Net-SNMP agents

Congestion Control Based on Distributed Statistical QoS-Aware Routing Management

Network coding -based congestion control at network layer : protocol design and evaluation

Cross Layer QoS Implementations: The Clean-Slate Approach