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Conference papers

Computational intelligence for cloud management: current trends and opportunities

Alexandru-Adrian Tantar 1 Anh Quan Nguyen 2 Pascal Bouvry 3 Bernabé Dorronsoro 2 El-Ghazali Talbi 4
1 ALEA - Advanced Learning Evolutionary Algorithms
CNRS - Centre National de la Recherche Scientifique : UMR5251, UB - Université de Bordeaux, Inria Bordeaux - Sud-Ouest
4 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : 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.
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Submitted on : Sunday, June 23, 2013 - 12:18:03 AM
Last modification on : Thursday, January 20, 2022 - 5:27:49 PM



Alexandru-Adrian Tantar, Anh Quan Nguyen, Pascal Bouvry, Bernabé Dorronsoro, El-Ghazali Talbi. Computational intelligence for cloud management: current trends and opportunities. CEC 2013 - IEEE Congress on Evolutionary Computation, Jun 2013, Cancun, Mexico. ⟨10.1109/CEC.2013.6557713⟩. ⟨hal-00837568⟩



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