32 research outputs found
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
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
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful
information from historical records and then determines future values. Learning
long-range dependencies that are embedded in time series is often an obstacle
for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a
specific kind of scheme in deep learning, promise to effectively overcome the
problem. In this article, we first give a brief introduction to the structure
and forward propagation mechanism of the LSTM model. Then, aiming at reducing
the considerable computing cost of LSTM, we put forward the Random Connectivity
LSTM (RCLSTM) model and test it by predicting traffic and user mobility in
telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic
connectivity between neurons, which achieves a significant breakthrough in the
architecture formation of neural networks. In this way, the RCLSTM model
exhibits a certain level of sparsity, which leads to an appealing decrease in
the computational complexity and makes the RCLSTM model become more applicable
in latency-stringent application scenarios. In the field of telecommunication
networks, the prediction of traffic series and mobility traces could directly
benefit from this improvement as we further demonstrate that the prediction
accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how
we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference
Robust On-line Matrix Completion on Graphs
We study online robust matrix completion on graphs. At each iteration a
vector with some entries missing is revealed and our goal is to reconstruct it
by identifying the underlying low-dimensional subspace from which the vectors
are drawn. We assume there is an underlying graph structure to the data, that
is, the components of each vector correspond to nodes of a certain (known)
graph, and their values are related accordingly. We give algorithms that
exploit the graph to reconstruct the incomplete data, even in the presence of
outlier noise. The theoretical properties of the algorithms are studied and
numerical experiments using both synthetic and real world datasets verify the
improved performance of the proposed technique compared to other state of the
art algorithms
Combined Intra- and Inter-domain Traffic Engineering using Hot-Potato Aware Link Weights Optimization
A well-known approach to intradomain traffic engineering consists in finding
the set of link weights that minimizes a network-wide objective function for a
given intradomain traffic matrix. This approach is inadequate because it
ignores a potential impact on interdomain routing. Indeed, the resulting set of
link weights may trigger BGP to change the BGP next hop for some destination
prefixes, to enforce hot-potato routing policies. In turn, this results in
changes in the intradomain traffic matrix that have not been anticipated by the
link weights optimizer, possibly leading to degraded network performance.
We propose a BGP-aware link weights optimization method that takes these
effects into account, and even turns them into an advantage. This method uses
the interdomain traffic matrix and other available BGP data, to extend the
intradomain topology with external virtual nodes and links, on which all the
well-tuned heuristics of a classical link weights optimizer can be applied. A
key innovative asset of our method is its ability to also optimize the traffic
on the interdomain peering links. We show, using an operational network as a
case study, that our approach does so efficiently at almost no extra
computational cost.Comment: 12 pages, Short version to be published in ACM SIGMETRICS 2008,
International Conference on Measurement and Modeling of Computer Systems,
June 2-6, 2008, Annapolis, Maryland, US
Scalable Tensor Factorizations for Incomplete Data
The problem of incomplete data - i.e., data with missing or unknown values -
in multi-way arrays is ubiquitous in biomedical signal processing, network
traffic analysis, bibliometrics, social network analysis, chemometrics,
computer vision, communication networks, etc. We consider the problem of how to
factorize data sets with missing values with the goal of capturing the
underlying latent structure of the data and possibly reconstructing missing
values (i.e., tensor completion). We focus on one of the most well-known tensor
factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In
the presence of missing data, CP can be formulated as a weighted least squares
problem that models only the known entries. We develop an algorithm called
CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization
approach to solve the weighted least squares problem. Based on extensive
numerical experiments, our algorithm is shown to successfully factorize tensors
with noise and up to 99% missing data. A unique aspect of our approach is that
it scales to sparse large-scale data, e.g., 1000 x 1000 x 1000 with five
million known entries (0.5% dense). We further demonstrate the usefulness of
CP-WOPT on two real-world applications: a novel EEG (electroencephalogram)
application where missing data is frequently encountered due to disconnections
of electrodes and the problem of modeling computer network traffic where data
may be absent due to the expense of the data collection process
Structural Analysis of Network Traffic Matrix via Relaxed Principal Component Pursuit
The network traffic matrix is widely used in network operation and
management. It is therefore of crucial importance to analyze the components and
the structure of the network traffic matrix, for which several mathematical
approaches such as Principal Component Analysis (PCA) were proposed. In this
paper, we first argue that PCA performs poorly for analyzing traffic matrix
that is polluted by large volume anomalies, and then propose a new
decomposition model for the network traffic matrix. According to this model, we
carry out the structural analysis by decomposing the network traffic matrix
into three sub-matrices, namely, the deterministic traffic, the anomaly traffic
and the noise traffic matrix, which is similar to the Robust Principal
Component Analysis (RPCA) problem previously studied in [13]. Based on the
Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated
Proximal Gradient (APG) algorithm, we present an iterative approach for
decomposing a traffic matrix, and demonstrate its efficiency and flexibility by
experimental results. Finally, we further discuss several features of the
deterministic and noise traffic. Our study develops a novel method for the
problem of structural analysis of the traffic matrix, which is robust against
pollution of large volume anomalies.Comment: Accepted to Elsevier Computer Network