649 research outputs found
A Note on Power-Laws of Internet Topology
The three Power-Laws proposed by Faloutsos et al(1999) are important
discoveries among many recent works on finding hidden rules in the seemingly
chaotic Internet topology. In this note, we want to point out that the first
two laws discovered by Faloutsos et al(1999, hereafter, {\it Faloutsos' Power
Laws}) are in fact equivalent. That is, as long as any one of them is true, the
other can be derived from it, and {\it vice versa}. Although these two laws are
equivalent, they provide different ways to measure the exponents of their
corresponding power law relations. We also show that these two measures will
give equivalent results, but with different error bars. We argue that for nodes
of not very large out-degree( in our simulation), the first Faloutsos'
Power Law is superior to the second one in giving a better estimate of the
exponent, while for nodes of very large out-degree() the power law
relation may not be present, at least for the relation between the frequency of
out-degree and node out-degree.Comment: 16 pages, 3 figure
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 critical look at power law modelling of the Internet
This paper takes a critical look at the usefulness of power law models of the
Internet. The twin focuses of the paper are Internet traffic and topology
generation. The aim of the paper is twofold. Firstly it summarises the state of
the art in power law modelling particularly giving attention to existing open
research questions. Secondly it provides insight into the failings of such
models and where progress needs to be made for power law research to feed
through to actual improvements in network performance.Comment: To appear Computer Communication
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