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Kaporis A.C.,University of Patras | Spirakis P.G.,Research Academic Computer Technology Institute
Computer Science Review | Year: 2011

A picturesque way to see a large network of links shared by many infinitesimally small selfish users is as a large pipeline infrastructure with users as liquid molecules flowing into it. When the owner of such a selfishly congested network tries to improve its flow speed, the common sense suggests to focus and fix links that seem older and slower. Contrary to this belief, Braess's paradox illustrates that destroying a part of a network, even of the most expensive infrastructure, can improve its performance. So a wise owner should take steps cautiously and benefit by exploiting the nature of this paradox. There are a few natural approaches for improving network performance. A simple approach, not requiring any network modifications, is Stackelberg routing. The network owner dictatorially controls a small fraction of flow, aiming to improve the induced routing performance of the remaining selfish flow. Unfortunately, there are examples of unboundedly bad performance under any possible control attempt made by the owner. Another side-effect is that the dictatorially controlled flow is usually sacrificed through slower paths, compared to the latency faced by the remaining free flow. An alternative approach is to introduce economic incentives, usually modeled as flow-dependent per-unit-of-flow tolls, that influence the users' selfish choices toward improving performance. However, the idea of tolls is not appealing to the users, since large tolls increase the users' disutility: routing time plus tolls paid. A simple and easy to implement way out from the above side effects is to exploit the essence of Braess's paradox toward improving network performance. In this work we survey some recent results about this paradox, eluding some recent hardness results under the most wide and natural assumptions about the link latencies of input network. © 2011 Elsevier Inc. Source

Bouboulis P.,National and Kapodistrian University of Athens | Theodoridis S.,National and Kapodistrian University of Athens | Theodoridis S.,Research Academic Computer Technology Institute
IEEE Transactions on Signal Processing | Year: 2011

Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the reproducing kernel Hilbert space (RKHS). However, so far, the emphasis has been on batch techniques. It is only recently, that online techniques have been considered in the context of adaptive signal processing tasks. Moreover, these efforts have only been focussed on real valued data sequences. To the best of our knowledge, no adaptive kernel-based strategy has been developed, so far, for complex valued signals. Furthermore, although the real reproducing kernels are used in an increasing number of machine learning problems, complex kernels have not, yet, been used, in spite of their potential interest in applications that deal with complex signals, with Communications being a typical example. In this paper, we present a general framework to attack the problem of adaptive filtering of complex signals, using either real reproducing kernels, taking advantage of a technique called complexification of real RKHSs, or complex reproducing kernels, highlighting the use of the complex Gaussian kernel. In order to derive gradients of operators that need to be defined on the associated complex RKHSs, we employ the powerful tool of Wirtinger's Calculus, which has recently attracted attention in the signal processing community. Wirtinger's calculus simplifies computations and offers an elegant tool for treating complex signals. To this end, in this paper, the notion of Wirtinger's calculus is extended, for the first time, to include complex RKHSs and use it to derive several realizations of the complex kernel least-mean-square (CKLMS) algorithm. Experiments verify that the CKLMS offers significant performance improvements over several linear and nonlinear algorithms, when dealing with nonlinearities. © 2010 IEEE. Source

Garofalakis J.,University of Patras | Garofalakis J.,Research Academic Computer Technology Institute | Stergiou E.,ATEI of Epirus
Performance Evaluation | Year: 2010

Nowadays, since the proportion of multicast traffic has increased compared to that of unicast traffic, the need for Multilayer Multistage Interconnection Layers Networks (MLNINs) has become more intense. In this paper a thorough evaluation of the performance of MLMINs using an analytical model is presented, as such an evaluation has not previously been developed. The multicasting policy that is used by MLMIN queues is the "partial multicast" and all the MLMINs studied use the "Cell Replication While Routing" (CRWR) technique. The performance model was applied under different offered loads to various network size MLMINs supporting various proportions of unicast and multicast traffic. The results have been confirmed in some marginal cases by existing work and the study reveals quantitatively the improvement in the performance metrics of MLMINs compared to the corresponding single-layer MINs. The findings of this paper are important as they could be useful in building optimum networks regarding their performance. © 2010 Elsevier B.V. Source

Garofalakis J.,University of Patras | Garofalakis J.,Research Academic Computer Technology Institute | Stergiou E.,ATEI of Epirus
Future Generation Computer Systems | Year: 2013

The aim of this paper is to develop an analytical method for performance evaluation of double prioritized Multistage Interconnected Networks (MINs) with single or multilayers and backpressure operation which provide service differentiation and QoS guarantee to an end application running over next generation Internet or Grid systems. Specifically, a new architecture of switching elements is used for the construction of MINs. This switch element uses two parallel queues in order to serve dual priority traffic. Besides this, uniform traffic conditions are presupposed and the bulk of packet arrivals in each cycle to the network inputs follow a Bernoulli distribution. A new analytical model for evaluating single buffered MIN's with 2×2 special switching elements supporting internally two classes' priority traffic is presented. Equations for the steady state are derived. These equations are then used in finding the most important multistage network performance metrics, such as throughput, and packet latency. The results are also validated using simulation and compared with previous related work in marginal cases. This proposed analytical model is accurate for various network sizes and various values of offered traffic to the multistage network inputs. © 2012 Published by Elsevier B.V. All rights reserved. Source

Bouboulis P.,National and Kapodistrian University of Athens | Slavakis K.,University of Peloponnese | Theodoridis S.,Research Academic Computer Technology Institute
IEEE Transactions on Neural Networks and Learning Systems | Year: 2012

This paper presents a wide framework for non-linear online supervised learning tasks in the context of complex valued signal processing. The (complex) input data are mapped into a complex reproducing kernel Hilbert space (RKHS), where the learning phase is taking place. Both pure complex kernels and real kernels (via the complexification trick) can be employed. Moreover, any convex, continuous and not necessarily differentiable function can be used to measure the loss between the output of the specific system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. In order to derive analytically the subgradients, the principles of the (recently developed) Wirtinger's calculus in complex RKHS are exploited. Furthermore, both linear and widely linear (in RKHS) estimation filters are considered. To cope with the problem of increasing memory requirements, which is present in almost all online schemes in RKHS, the sparsification scheme, based on projection onto closed balls, has been adopted. We demonstrate the effectiveness of the proposed framework in a non-linear channel identification task, a non-linear channel equalization problem and a quadrature phase shift keying equalization scheme, using both circular and non circular synthetic signal sources. © 2012 IEEE. Source

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