703 research outputs found
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
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
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