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

NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN

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 long-range 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 GEEANT network, we show that our model converges quickly and gives state of the art TM prediction performance.Comment: Submitted to NOMS18. arXiv admin note: substantial text overlap with arXiv:1705.0569

NeuRoute: Predictive Dynamic Routing for Software-Defined Networks

This paper introduces NeuRoute, a dynamic routing framework for Software Defined Networks (SDN) entirely based on machine learning, specifically, Neural Networks. Current SDN/OpenFlow controllers use a default routing based on Dijkstra algorithm for shortest paths, and provide APIs to develop custom routing applications. NeuRoute is a controller-agnostic dynamic routing framework that (i) predicts traffic matrix in real time, (ii) uses a neural network to learn traffic characteristics and (iii) generates forwarding rules accordingly to optimize the network throughput. NeuRoute achieves the same results as the most efficient dynamic routing heuristic but in much less execution time.Comment: Accepted for CNSM 201

On the Minimization of Handover Decision Instability in Wireless Local Area Networks

This paper addresses handover decision instability which impacts negatively on both user perception and network performances. To this aim, a new technique called The HandOver Decision STAbility Technique (HODSTAT) is proposed for horizontal handover in Wireless Local Area Networks (WLAN) based on IEEE 802.11standard. HODSTAT is based on a hysteresis margin analysis that, combined with a utilitybased function, evaluates the need for the handover and determines if the handover is needed or avoided. Indeed, if a Mobile Terminal (MT) only transiently hands over to a better network, the gain from using this new network may be diminished by the handover overhead and short usage duration. The approach that we adopt throughout this article aims at reducing the minimum handover occurrence that leads to the interruption of network connectivity (this is due to the nature of handover in WLAN which is a break before make which causes additional delay and packet loss). To this end, MT rather performs a handover only if the connectivity of the current network is threatened or if the performance of a neighboring network is really better comparing the current one with a hysteresis margin. This hysteresis should make a tradeoff between handover occurrence and the necessity to change the current network of attachment. Our extensive simulation results show that our proposed algorithm outperforms other decision stability approaches for handover decision algorithm.Comment: 13 Pages, IJWM

Limitations of OpenFlow Topology Discovery Protocol

OpenFlow Discovery Protocol (OFDP) is the de-facto protocol used by OpenFlow controllers to discover the underlying topology. In this paper, we show that OFDP has some serious security, efficiency and functionality limitations that make it non suitable for production deployments. Instead, we briefly introduce sOFTD, a new discovery protocol with a built-in security characteristics and which is more efficient than traditional OFDP.Comment: The peer reviewed version can be found here (to be published soon

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Comparison of some end-to-end flow control policies in a packet switching network

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A novel architecture for utility driven management

In this paper, we specify and implement a framework for utility driven generation and scheduling of management actions based on Business context and Service Level Agreements (SLAs). SLAs are compiled into low level management policies; as well as sets of performance metrics and utility functions. These are subsequently used to drive the scheduling of the low level policy actions. Each action is associated with a utility participation value based on parameters relevant to the contract(s) it is related to; as well as the run-time context of its triggering and execution times. A Web hosting company case study is used to illustrate the benefit of taking into account business level implications when scheduling the execution of management tasks. We measure the overall business profitability as a pondered linear function of other business metrics such as overall raw financial profit and overall customer satisfaction. Finally, we discuss the difficulties and challenges related to the correct estimation of utility costs associated with the low level management/control actions5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI