References of "Dorronsoro, Bernabé 40020186"
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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 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 detailMulti-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems
Guzek, Mateusz UL; Pecero, Johnatan UL; Dorronsoro, Bernabé UL et al

in Applied Soft Computing (2014), 24

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches ... [more ▼]

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms. The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance. [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 detailA Power Efficient Genetic Algorithm for Resource Allocation in Cloud Computing Data Centers
Portaluri, Giuseppe; Giordano, Stefano; Kliazovich, Dzmitry UL et al

in IEEE International Conference on Cloud Networking (CLOUDNET), Luxembourg City, 2014. (2014)

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See detailEvolutionary Algorithms for Mobile Ad Hoc Networks
Dorronsoro, Bernabé UL; Ruiz, Patricia UL; Danoy, Grégoire UL et al

Book published by John Wiley & Sons (2014)

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See detailIt's Not a Bug, It'sa Feature: Wait-free Asynchronous Cellular Genetic Algorithm
Pinel, Frédéric UL; Dorronsoro, Bernabé UL; Bouvry, Pascal UL et al

in Wyrzykowski, Roman; Dongarra, Jack (Eds.) Parallel Processing and Applied Mathematics 10th International Conference, PPAM 2013 Warsaw, Poland, September 8–11, 2013 (2014)

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See detailSavant: Automatic parallelization of a scheduling heuristic with machine learning
Pinel, Frédéric UL; Dorronsoro, Bernabé UL; Bouvry, Pascal UL et al

in Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on (2013, August 13)

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See detailSolving very large instances of the scheduling of independent tasks problem on the GPU
Pinel, Frédéric UL; Dorronsoro, Bernabé UL; Bouvry, Pascal UL

in Journal of Parallel & Distributed Computing (2013), 73(1), 101-110

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See detailAn Overlay Approach for Optimising Small-World Properties in VANETs
Schleich, Julien UL; Danoy, Grégoire UL; Dorronsoro, Bernabé UL et al

in Proceedings of the 16th European Conference on Applications of Evolutionary Computation (EvoApplications) (2013)

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See detailEvolutionary algorithms based on game theory and cellular automata with coalitions
Dorronsoro, Bernabé UL; Burguillo, Juan Carlos; Peleteiro, Ana et al

in Zelinka, I.; Snasel, V.; Abraham, A. (Eds.) Handbook of Optimization (2013)

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized ... [more ▼]

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems. [less ▲]

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See detailAchieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution
Dorronsoro, Bernabé UL; Danoy, Grégoire UL; Nebro, Antonio J. et al

in Computers & Operations Research (2013), 40(6), 1552-1563

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See detailMemetic Algorithms for Energy-Aware Computation and Communications Optimization in Computing Clusters
Pecero, Johnatan UL; Dorronsoro, Bernabé UL; Guzek, Mateusz UL et al

in Ahmad, Ishfaq; Ranka, Sanjay (Eds.) Handbook energy-aware and green computing (2012)

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See detailInformation Dissemination in VANETs based upon a Tree Topology
Ruiz, Patricia UL; Dorronsoro, Bernabé UL; Bouvry, Pascal UL et al

in Ad Hoc Networks (2012), 10(1), 111-127

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See detailStudy of Different Small-world Topology Generation Mechanisms for Genetic Algorithms
Dorronsoro, Bernabé UL; Bouvry, Pascal UL

in IEEE Congress on Evolutionary Computation (2012)

The use of small-world graphs as a topology structure for the population of Evolutionary Algorithms (EAs) has been recently proposed in the literature. The motivation is clear: the high clustering ... [more ▼]

The use of small-world graphs as a topology structure for the population of Evolutionary Algorithms (EAs) has been recently proposed in the literature. The motivation is clear: the high clustering coefficient and low characteristic path length of such networks makes them suitable for fast local information dissemination, while at the same time preventing it from quickly spreading on the whole population, as it happens in panmictic populations. However, even though several papers addressed this issue so far, only a few of them are able to provide competitive results with other panmictic and/or decentralized population EAs with similar configurations. Therefore, we perform ax study in this work, both theoretically and empirically, on the most appropriate mechanisms to generate SW topologies for Genetic Algorithms (a family of EA). The algorithms are analyzed in terms of efficiency and efficacy, and the best studied variant is validated versus other GAs using well known centralized and decentralized population structures, outperforming them. [less ▲]

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See detailA Multithreading Local Search For Multiobjective Energy-Aware Scheduling In Heterogeneous Computing Systems
Iturriaga, Santiago; Nesmachnow, Sergio; Dorronsoro, Bernabé UL

in European Conference on Modelling and Simulation (ECMS) (2012)

This article introduces an efficient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous computing systems considering the makespan and energy ... [more ▼]

This article introduces an efficient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous computing systems considering the makespan and energy consumption objectives. The proposed method follows a fully multiobjective approach using a Pareto-based dominance search executed in parallel. The experimental analysis demonstrates that the new multithreading algorithm outperforms a set of deterministic heuristics based on Min-Min. The new method is able to achieve significant improvements in both objectives in reduced execution times for a broad set of testbed instances. [less ▲]

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See detailDesigning a Self-organized Approach for Scheduling Bag-of-Tasks
Jimenez Laredo, Juan Luis UL; Dorronsoro, Bernabé UL; Pecero, Johnatan UL et al

in Designing a Self-organized Approach for Scheduling Bag-of-Tasks (2012)

This paper proposes a decentralized and self-organized agent system for dynamically load-balancing tasks arriving in the form of Bags-of-Tasks (BoTs) in large-scale decentralized systems. The approach is ... [more ▼]

This paper proposes a decentralized and self-organized agent system for dynamically load-balancing tasks arriving in the form of Bags-of-Tasks (BoTs) in large-scale decentralized systems. The approach is inspired by the emergent behavior of the sandpile model; a cellular automaton behaving at the edge of chaos. Depending on the state of the cellular automaton, rather different responses may occur when a new task is assigned to a resource. It may change nothing or generate avalanches that reconfigure the state of the system. The proportion between the abundance of avalanches and their sizes shows a power-law relation, a scale-invariant behavior that does not need to be tuned. That means that large –catastrophic– avalanches are very rare but small ones occur very often. Such a smart and emergent behavior fits well with the idea of non-clairvoyant scheduling, where tasks are load balanced into computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. In order to study the viability of the approach, we have conducted an empirical experimentation which shows that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures. [less ▲]

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See detailNovel efficient asynchronous cooperative co-evolutionary multi-objective algorithms
Nielsen, Sune Steinbjorn UL; Dorronsoro, Bernabé UL; Danoy, Grégoire UL et al

in Congress on Evolutionary Computation (CEC) (2012)

Detailed reference viewed: 28 (4 UL)