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Alexandru Tantar

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See detailVoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms
Simionovici, Ana-Maria UL; Tantar, Alexandru; Bouvry, Pascal UL et al

Scientific Conference (2015, December 05)

The paper deals with an important problem in the Voice over IP (VoIP) domain, namely being able to understand and predict the structure of traffic over some given period of time. VoIP traffic has a time ... [more ▼]

The paper deals with an important problem in the Voice over IP (VoIP) domain, namely being able to understand and predict the structure of traffic over some given period of time. VoIP traffic has a time variant structure, e.g. due to sudden peaks, daily or weekly moving patterns of activities, which in turn makes prediction difficult. Obtaining insights about the structure and trends of traffic has important implications when dealing with the nowadays cloud-deployed VoIP services. Prediction techniques are applied to anticipate the incoming traffic, for an efficient distribution of the traffic in the system and allocation of resources. The article looks in a critical manner at a series of machine learning techniques. We namely compare and review (using real VoIP data) the results obtained when using a Gaussian Mixture Model (GMM), Gaussian Processes (GP), and an evolutionary like Interacting Particle Systems based (sampling) algorithm. The experiments consider different setups as to verify the time variant traffic assumption. [less ▲]

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See detailoptPBN: An Optimisation Toolbox for Probabilistic Boolean Networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in PLoS ONE (2014), 9(7), 980011-15

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only ... [more ▼]

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. Results We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network. Summary The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks. [less ▲]

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See detailA survey on sustainability in ICT a computing perspective
Tantar, Alexandru-Adrian UL; Tantar, Emilia UL

in GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (2014)

The rise of the data centers industry, together with the emergence of large cloud computing that require large quantities of resources to be maintained, brought the need of providing a sustainable ... [more ▼]

The rise of the data centers industry, together with the emergence of large cloud computing that require large quantities of resources to be maintained, brought the need of providing a sustainable development process. Through this paper we aim to provide an introductory insight on the status and tools available to tackle this perspective within the evolutionary and genetic algorithms community. Existing advancement are also emphasized and perspectives outlined. [less ▲]

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See detailAsymmetric quadratic landscape approximation model
Tantar, Alexandru-Adrian UL; Tantar, Emilia UL; Schütze, O.

in GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference (2014)

This work presents an asymmetric quadratic approximation model and an ε-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in ... [more ▼]

This work presents an asymmetric quadratic approximation model and an ε-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The ε-archiving algorithm is designed to maintain and update a set of such asymmetric descriptors, spaced at some given threshold distance. An in-depth analysis is conducted on the stability and performance of the asymmetric model, comparing the results with the ones obtained by a quadratic polynomial approximation. A series of different applications are possible in areas such as dynamic and robust optimization. © 2014 ACM. [less ▲]

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See detailDesign of an Energy Efficiency Model and Architecture for Cloud Management using Prediction Models
Nguyen, Anh Quan UL; Tantar, Alexandru-Adrian UL; Bouvry, Pascal UL et al

Scientific Conference (2013, December 18)

In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model ... [more ▼]

In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model and the validation will be performed on OpenStack. This paper intends to be a position paper, the implementation and experimental run will be conducted in future work. The design concept leverages the prediction model by providing a full architecture binding the resource demands, the predictions and the actual cloud environment (Openstack). The prediction analysis feeds the power-aware agents that run on the compute nodes in order to turn the nodes into sleep mode when the load state is low to reduce the energy consumption of the data center. [less ▲]

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See detailPredictive Modeling in a VoIP System
Simionovici, Ana-Maria UL; Tantar, Alexandru; Bouvry, Pascal UL et al

in Journal of Telecommunications and Information Technology (2013), 4

An important problem one needs to deal with in a Voice over IP system is server overload. One way for pre- venting such problems is to rely on prediction techniques for the incoming traffic, namely as to ... [more ▼]

An important problem one needs to deal with in a Voice over IP system is server overload. One way for pre- venting such problems is to rely on prediction techniques for the incoming traffic, namely as to proactively scale the avail- able resources. Anticipating the computational load induced on processors by incoming requests can be used to optimize load distribution and resource allocation. In this study, the authors look at how the user profiles, peak hours or call pat- terns are shaped for a real system and, in a second step, at constructing a model that is capable of predicting trends. [less ▲]

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See detailEnergy Efficiency Metaheuristic Mechanism for Cloud Broker \\ in Multi-Cloud Computing
Nguyen, Anh Quan UL; Tantar, Alexandru-Adrian UL; Bouvry, Pascal UL et al

Scientific Conference (2013, July 13)

In this paper, we would like to present our view on an energy efficiency mechanism based on a metaheuristic algorithm for a cloud broker in multi-cloud computing. The following study is only a design ... [more ▼]

In this paper, we would like to present our view on an energy efficiency mechanism based on a metaheuristic algorithm for a cloud broker in multi-cloud computing. The following study is only a design concept and therefore this paper does not intend offering some established results. The metaheuristic based algorithm we envisage using needs to deal with the multiple objectives defined by the cloud users and the Cloud Service Providers (CSPs). The goal of the mechanism mainly focuses on energy efficiency while searching for a balance point that satisfies the objectives of both the cloud users and the CSPs. In our proposed concept, the designed mechanism needs to include a component to collect the resources that underutilized by the cloud users (in public or private cloud environment) and offers them back to the cloud broker for re-rent. [less ▲]

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See detailRecent development and biomedical applications of probabilistic Boolean networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in Cell Communication and Signaling (2013), 11(46),

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based ... [more ▼]

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. [less ▲]

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See detailDynamic MixVoIP
Simionovici, Ana-Maria UL; Tantar, Alexandru; Bouvry, Pascal UL

Scientific Conference (2013, July)

Dynamic optimization based on incoming load analysis and prediction is considered to be an innovative approach in order to prevent the overload of the servers in a Voice over IP system. The ongoing ... [more ▼]

Dynamic optimization based on incoming load analysis and prediction is considered to be an innovative approach in order to prevent the overload of the servers in a Voice over IP system. The ongoing project is in an early stage of study and the followings are the current vision and concept regarding it. The information gathered by inspecting the real system of an IT company, MixVoIP, (probe server and sensors spread inside the cloud) and by analyzing the data provided by the predictive algorithm, will be used to optimize load distribution and resource allocation. The implementation in the real-life environment should lead to an improvement of the service offered but also to a sensible reduction of the associated carbon emissions, e.g. as a result of an improved load management, reduced idle CPU times or optimally exploited resources. [less ▲]

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See detailComputational Intelligence for Cloud Management Current Trends and Opportunities
Tantar, Alexandru-Adrian UL; Nguyen, Anh Quan UL; Bouvry, Pascal UL et al

Scientific Conference (2013, June 21)

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were ... [more ▼]

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend towards using many-objective stochastic models became apparent over the past years. This work reviews in brief some of the more recent studies on cloud computing modeling and optimization, and points at notions on stability, convergence, definitions or results that could serve to analyze, respectively build accurate cloud computing models. A very brief discussion of simulation frameworks that include support for energy-aware components is also given. [less ▲]

Detailed reference viewed: 36 (1 UL)
See detailSpecial issue on evolutionary computing and complex systems
Bouvry, Pascal UL; Schuetze, Oliver; Coello Coello, Carlos A. et al

in SOFT COMPUTING (2013), 17(6), 909-912

Detailed reference viewed: 42 (5 UL)
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See detailOn the Foundations and the Applications of Evolutionary Computing
Del Moral, Pierre; Tantar, Alexandru-Adrian UL; Tantar, Emilia UL

in Studies in Computational Intelligence (2013), 447

Genetic type particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics ... [more ▼]

Genetic type particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics, information theory, and engineering sciences. Understanding rigorously these new Monte Carlo simulation tools leads to fascinating mathematics related to Feynman-Kac path integral theory and their interacting particle interpretations. In this chapter, we provide an introduction to the stochastic modeling and the theoretical analysis of these particle algorithms. We also illustrate these methods through several applications. [less ▲]

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See detailEVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation IV [EVOLVE 2013, Leiden The Netherlands, July 10-13, 2013]
Emmerich, Michael; Deutz, Andre; Schuetze, Oliver et al

Book published by Springer (2013)

Detailed reference viewed: 39 (5 UL)
See detailEVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Schütze, O.; Coello, C. A. C.; Bouvry, Pascal UL et al

Book published by Springer (2012)

Detailed reference viewed: 30 (0 UL)
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See detailA classification of dynamic multi-objective optimization problems
Tantar, Alexandru-Adrian UL; Tantar, Emilia UL; Bouvry, Pascal UL

in A classification of dynamic multi-objective optimization problems (2011)

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See detailLoad balancing for sustainable ICT
Tantar, Alexandru-Adrian UL; Tantar, Emilia UL; Bouvry, Pascal UL

in Load balancing for sustainable ICT (2011)

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See detailSparse Antenna Array Optimization with the Cross-Entropy Method
Minvielle, Pierre; Tantar, Emilia UL; Tantar, Alexandru-Adrian UL et al

in IEEE Transactions on Antennas & Propagation (2011), 59(8), 2862-2871

The interest in sparse antenna arrays is growing, mainly due to cost concerns, array size limitations, etc. Formally, it can be shown that their design can be expressed as a constrained multidimensional ... [more ▼]

The interest in sparse antenna arrays is growing, mainly due to cost concerns, array size limitations, etc. Formally, it can be shown that their design can be expressed as a constrained multidimensional nonlinear optimization problem. Generally, through lack of convex property, such a multiextrema problem is very tricky to solve by usual deterministic optimization methods. In this article, a recent stochastic approach, called Cross-Entropy method, is applied to the continuous constrained design problem. The method is able to construct a random sequence of solutions which converges probabilistically to the optimal or the near-optimal solution. Roughly speaking, it performs adaptive changes to probability density functions according to the Kullback-Leibler cross-entropy. The approach efficiency is illustrated in the design of a sparse antenna array with various requirements. [less ▲]

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See detailOn dynamic multi-objective optimization - classification and performance measures
Tantar, Emilia UL; Tantar, Alexandru-Adrian UL; Bouvry, Pascal UL

in On dynamic multi-objective optimization - classification and performance measures (2011)

In this work we focus on defining how dynamism can be modeled in the context of multi-objective optimization. Based on this, we construct a component oriented classification for dynamic multi-objective ... [more ▼]

In this work we focus on defining how dynamism can be modeled in the context of multi-objective optimization. Based on this, we construct a component oriented classification for dynamic multi-objective optimization problems. For each category we provide synthetic examples that depict in a more explicit way the defined model. We do this either by positioning existing synthetic benchmarks with respect to the proposed classification or through new problem formulations. In addition, an online dynamic MNK-landscape formulation is introduced together with a new comparative metric for the online dynamic multi-objective context. [less ▲]

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