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Autor:
Azzouni, Abdelhadi, Pujolle, Guy
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 prev
Externí odkaz:
http://arxiv.org/abs/1710.06799
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
Externí odkaz:
http://arxiv.org/abs/1709.06002
Autor:
Azzouni, Abdelhadi, Pujolle, Guy
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-Ter
Externí odkaz:
http://arxiv.org/abs/1705.05690
Topology discovery is one of the most critical tasks of Software-Defined Network (SDN) controllers. Current SDN controllers use the OpenFlow Discovery Protocol (OFDP) as the de-facto protocol for discovering the underlying network topology. In a prev
Externí odkaz:
http://arxiv.org/abs/1705.04527
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 suitab
Externí odkaz:
http://arxiv.org/abs/1705.00706