References of "Talbi, El-Ghazali"
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See detailForeword
Danoy, Grégoire UL; Jourdan, Laetitia; Talbi, El-Ghazali et al

in International (2016)

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See detailMetaheuristics for the Virtual Machine Mapping Problem in Clouds
Nesmachnow, Sergio; Dorronsoro, Bernabé UL; Talbi, El-Ghazali et al

in Informatica, Lith. Acad. Sci. (2015), 26(1), 111-134

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into prebooked ... [more ▼]

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into prebooked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are studied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutionary Algorithm. The experimental evaluation over instances accounting for workloads and scenarios using real data from cloud providers, indicates that the parallel hybrid Evolutionary Algorithm is the best method to solve the problem, computing solutions with up to 368.9% profit improvement over greedy heuristics results while accounting for accurate makespan and flowtime values. [less ▲]

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See detailA Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing
Guzek, Mateusz UL; Bouvry, Pascal UL; Talbi, El-Ghazali

in IEEE Computational Intelligence Magazine (2015), 10(2), 53-67

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are ... [more ▼]

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems. [less ▲]

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See detailFinding a robust configuration for the AEDB information dissemination protocol for mobile ad hoc networks
Ruiz, Patricia; Dorronsoro, Bernabé UL; Talbi, El-Ghazali et al

in Applied Soft Computing (2015), 32

The Adaptive Enhanced Distance Based Broadcasting Protocol, AEDB hereinafter, is an advanced adaptive protocol for information dissemination in mobile ad hoc networks (MANETs). It is based on the Distance ... [more ▼]

The Adaptive Enhanced Distance Based Broadcasting Protocol, AEDB hereinafter, is an advanced adaptive protocol for information dissemination in mobile ad hoc networks (MANETs). It is based on the Distance Based broadcasting protocol, and it acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. As most of the existing communication protocols, AEDB relies on different thresholds for adapting its behavior to the environment. We propose in this work to look for configurations that induce a stable performance of the protocol in different networks by automatically fine tuning these thresholds thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. Finding robust solutions for this problem is important because MANETs have a highly unpredictable and dynamic topology, features that have a strong influence on the performance of the protocol. Consequently, robust solutions that show a good performance under any circumstances are required. In this work, we define different fitness functions that measure robustness of solutions for better guiding the algorithm towards more robust solutions. They are: median, constrained, worst coverage, and worst hypervolume. Results show, that the two worst-case approaches perform better, not only in case of robustness but also in terms of accuracy of the reported AEDB configurations on a large set of networks. [less ▲]

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See detailOptimizing communication satellites payload configuration with exact approaches
Stathakis, Apostolos UL; Danoy, Grégoire UL; Bouvry, Pascal UL et al

in Engineering Optimization (2015), 0(0), 1-26

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See detailAn NK Landscape Based Model Mimicking the Protein Inverse Folding Problem
Nielsen, Sune Steinbjorn UL; Danoy, Grégoire UL; Talbi, El-Ghazali et al

in 27th European Conference on Operational Research (EURO) (2015)

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See detailNIDISC Introduction and Committees
Bouvry, Pascal UL; Danoy, Grégoire UL; Seredynski, Franciszek et al

in Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2015 IEEE International 2015-10-27 14:21:42 +0000 2015-10-27 14:21:42 +0000 (2015)

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See detailA Novel Multi-objectivisation Approach for Optimising the Protein Inverse Folding Problem
Nielsen, Sune Steinbjorn UL; Danoy, Grégoire UL; Jurkowski, Wiktor et al

in Applications of Evolutionary Computation: 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings (2015)

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding ... [more ▼]

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding Problem (IFP) is in itself an important research problem, but also at the heart of most rational protein design approaches. In brief, the IFP consists in finding sequences that will fold into a given structure, rather than determining the structure for a given sequence - as in conventional structure prediction. In this work we present a Multi Objective Genetic Algorithm (MOGA) using the diversity-as-objective (DAO) variant of multi-objectivisation, to optimise secondary structure similarity and sequence diversity at the same time, hence pushing the search farther into wide-spread areas of the sequence solution-space. To control the high diversity generated by the DAO approach, we add a novel Quantile Constraint (QC) mechanism to discard an adjustable worst quantile of the population. This DAO-QC approach can efficiently emphasise exploitation rather than exploration to a selectable degree achieving a trade-off producing both better and more diverse sequences than the standard Genetic Algorithm (GA). To validate the final results, a subset of the best sequences was selected for tertiary structure prediction. The super-positioning with the original protein structure demonstrated that meaningful sequences are generated underlining the potential of this work. [less ▲]

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See detailNK Landscape Instances Mimicking the Protein Inverse Folding Problem Towards Future Benchmarks
Nielsen, Sune Steinbjorn UL; Danoy, Grégoire UL; Bouvry, Pascal UL et al

in GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (2015)

This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing ... [more ▼]

This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing on a simpler secondary structure version. Through landscape analysis tests, numerous problem properties are identified and used to parameterise and validate model instances in terms of epistatic links, adaptive- and random walk characteristics. Then the performance of different Genetic Algorithms (GAs) is compared on both the new NK Models and the original IFP, in terms of population diversity, solution quality and convergence characteristics. It is demonstrated that very similar properties are captured in all presented tests with a significantly faster evaluation time compared to the real IFP. The future purpose of such a model is to provide a generic benchmark for algorithms targeting protein sequence optimisation, specifically in protein design. It may also provide the foundation for more in-depth studies of the size, shape and characteristics of the solution space of good solutions to the IFP. [less ▲]

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See detailMulti-Objective Evolutionary Approach for the Satellite Payload Power Optimization Problem
Kieffer, Emmanuel UL; Stathakis, Apostolos UL; Danoy, Grégoire UL et al

in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014) (2014, December)

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See detailBi-objective Optimization of Satellite Payload Power Configuration
Kieffer, Emmanuel UL; Stathakis, Apostolos UL; Danoy, Grégoire UL et al

in International Conference on Metaheuristics and Nature Inspired Computing (META 2014) (2014, October)

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See detailHybridisation Schemes for Communication Satellite Payload Configuration Optimisation
Stathakis, Apostolos; Danoy, Grégoire UL; Talbi, El-Ghazali et al

in 17th European Conference on Applications of Evolutionary Computation (EvoApplications 2014) (2014, April)

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See detailSolving the three dimensional quadratic assignment problem on a computational grid
Mezmaz, Mohand; Mehdi, Malika UL; Bouvry, Pascal UL et al

in Cluster Computing (2014), 17(2), 205-217

The exact resolution of large instances of combinatorial optimization problems, such as three dimensional quadratic assignment problem (Q3AP), is a real challenge for grid computing. Indeed, it is ... [more ▼]

The exact resolution of large instances of combinatorial optimization problems, such as three dimensional quadratic assignment problem (Q3AP), is a real challenge for grid computing. Indeed, it is necessary to reconsider the resolution algorithms and take into account the characteristics of such environments, especially large scale and dynamic availability of resources, and their multi-domain administration. In this paper, we revisit the design and implementation of the branch and bound algorithm for solving large combinatorial optimization problems such as Q3AP on the computational grids. Such gridification is based on new ways to effi- ciently deal with some crucial issues, mainly dynamic adaptive load balancing and fault tolerance. Our new approach allowed the exact resolution on a nation-wide grid of a dif- ficult Q3AP instance. To solve this instance, an average of 1,123 computing cores were used for less than 12 days with a peak of around 3,427 computing cores. [less ▲]

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See detailOptimizing AEDB Broadcasting Protocol with Parallel Multi-objective Cooperative Coevolutionary NSGAII
Dorronsoro, Bernabé UL; Ruiz, Patricia UL; Talbi, El-Ghazali et al

in Optimizing AEDB Broadcasting Protocol with Parallel Multi-objective Cooperative Coevolutionary NSGAII (2014)

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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 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 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 ▲]

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