43,416 research outputs found
The pseudo-self-similar traffic model: application and validation
Since the early 1990¿s, a variety of studies has shown that network traffic, both for local- and wide-area networks, has self-similar properties. This led to new approaches in network traffic modelling because most traditional traffic approaches result in the underestimation of performance measures of interest. Instead of developing completely new traffic models, a number of researchers have proposed to adapt traditional traffic modelling approaches to incorporate aspects of self-similarity. The motivation for doing so is the hope to be able to reuse techniques and tools that have been developed in the past and with which experience has been gained. One such approach for a traffic model that incorporates aspects of self-similarity is the so-called pseudo self-similar traffic model. This model is appealing, as it is easy to understand and easily embedded in Markovian performance evaluation studies. In applying this model in a number of cases, we have perceived various problems which we initially thought were particular to these specific cases. However, we recently have been able to show that these problems are fundamental to the pseudo self-similar traffic model. In this paper we review the pseudo self-similar traffic model and discuss its fundamental shortcomings. As far as we know, this is the first paper that discusses these shortcomings formally. We also report on ongoing work to overcome some of these problems
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel
relationships between time series that represent activities recorded at
different nodes of a highly complex system. In this paper, we introduce
multipoles, a novel class of linear relationships between more than two time
series. A multipole is a set of time series that have strong linear dependence
among themselves, with the requirement that each time series makes a
significant contribution to the linear dependence. We demonstrate that most
interesting multipoles can be identified as cliques of negative correlations in
a correlation network. Such cliques are typically rare in a real-world
correlation network, which allows us to find almost all multipoles efficiently
using a clique-enumeration approach. Using our proposed framework, we
demonstrate the utility of multipoles in discovering new physical phenomena in
two scientific domains: climate science and neuroscience. In particular, we
discovered several multipole relationships that are reproducible in multiple
other independent datasets and lead to novel domain insights.Comment: This is the accepted version of article submitted to IEEE
Transactions on Knowledge and Data Engineering 201
Modelling of self-similar teletraffic for simulation
Recent studies of real teletraffic data in modern computer networks have shown that teletraffic exhibits self-similar (or fractal) properties over a wide range of time scales. The properties of self-similar teletraffic are very different from the traditional models of teletraffic based on Poisson, Markov-modulated Poisson, and related processes. The use of traditional models in networks characterised by self-similar processes can lead to incorrect conclusions about the performance of analysed networks. These include serious over-estimations of the performance of computer networks, insufficient allocation of communication and data processing resources, and difficulties ensuring the quality of service expected by network users. Thus, full understanding of the self-similar nature in teletraffic is an important issue.
Due to the growing complexity of modern telecommunication networks, simulation has become the only feasible paradigm for their performance evaluation. In this thesis, we make some contributions to discrete-event simulation of networks with strongly-dependent, self-similar teletraffic.
First, we have evaluated the most commonly used methods for estimating the self-similarity parameter H using appropriately long sequences of data. After assessing properties of available H estimators, we identified the most efficient estimators for practical studies of self-similarity.
Next, the generation of arbitrarily long sequences of pseudo-random numbers possessing specific stochastic properties was considered. Various generators of pseudo-random self-similar sequences have been proposed. They differ in computational complexity and accuracy of the self-similar sequences they generate. In this thesis, we propose two new generators of self-similar teletraffic: (i) a generator based on Fractional Gaussian Noise and Daubechies Wavelets (FGN-DW), that is one of the fastest and the most accurate generators so far proposed; and (ii) a generator based on the Successive Random Addition (SRA) algorithm. Our comparative study of sequential and fixed-length self-similar pseudo-random teletraffic generators showed that the FFT, FGN-DW and SRP-FGN generators are the most efficient, both in the sense of accuracy and speed.
To conduct simulation studies of telecommunication networks, self-similar processes often need to be transformed into suitable self-similar processes with arbitrary marginal distributions. Thus, the next problem addressed was how well the self-similarity and autocorrelation function of an original self-similar process are preserved when the self-similar sequences are converted into suitable self-similar processes with arbitrary marginal distributions. We also show how pseudo-random self-similar sequences can be applied to produce a model of teletraffic associated with the transmission of VBR JPEG /MPEG video. A combined gamma/Pareto model based on the application of the FGN-DW generator was used to synthesise VBR JPEG /MPEG video traffic.
Finally, effects of self-similarity on the behaviour of queueing systems have been investigated. Using M/M/1/∞ as a reference queueing system with no long-range dependence, we have investigated how self-similarity and long-range dependence in arrival processes affect the length of sequential simulations being executed for obtaining steady-state results with the required level of statistical error. Our results show that the finite buffer overflow probability of a queueing system with self-similar input is much greater than the equivalent queueing system with Poisson or a short-range dependent input process, and that the overflow probability increases as the self-similarity parameter approaches one
Self-* overload control for distributed web systems
Unexpected increases in demand and most of all flash crowds are considered
the bane of every web application as they may cause intolerable delays or even
service unavailability. Proper quality of service policies must guarantee rapid
reactivity and responsiveness even in such critical situations. Previous
solutions fail to meet common performance requirements when the system has to
face sudden and unpredictable surges of traffic. Indeed they often rely on a
proper setting of key parameters which requires laborious manual tuning,
preventing a fast adaptation of the control policies. We contribute an original
Self-* Overload Control (SOC) policy. This allows the system to self-configure
a dynamic constraint on the rate of admitted sessions in order to respect
service level agreements and maximize the resource utilization at the same
time. Our policy does not require any prior information on the incoming traffic
or manual configuration of key parameters. We ran extensive simulations under a
wide range of operating conditions, showing that SOC rapidly adapts to time
varying traffic and self-optimizes the resource utilization. It admits as many
new sessions as possible in observance of the agreements, even under intense
workload variations. We compared our algorithm to previously proposed
approaches highlighting a more stable behavior and a better performance.Comment: The full version of this paper, titled "Self-* through self-learning:
overload control for distributed web systems", has been published on Computer
Networks, Elsevier. The simulator used for the evaluation of the proposed
algorithm is available for download at the address:
http://www.dsi.uniroma1.it/~novella/qos_web
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
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
We propose a convolutional network with hierarchical classifiers for
per-pixel semantic segmentation, which is able to be trained on multiple,
heterogeneous datasets and exploit their semantic hierarchy. Our network is the
first to be simultaneously trained on three different datasets from the
intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and
is able to handle different semantic level-of-detail, class imbalances, and
different annotation types, i.e. dense per-pixel and sparse bounding-box
labels. We assess our hierarchical approach, by comparing against flat,
non-hierarchical classifiers and we show improvements in mean pixel accuracy of
13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB
classes. Our implementation achieves inference rates of 17 fps at a resolution
of 520x706 for 108 classes running on a GPU.Comment: IEEE Intelligent Vehicles 201
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