TANTAR, Alexandru-Adrian; NGUYEN, Anh Quan; BOUVRY, Pascal; DORRONSORO, Bernabé; TALBI, El-Ghazali

doi:10.1109/CEC.2013.6557713

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TANTAR, Alexandru-Adrian
Advanced Learning Evolutionary Algorithms [ALEA]

NGUYEN, Anh Quan
Faculté des Sciences, de la Technologie et de la Communication [FSTC]

BOUVRY, Pascal

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Communication dans un congrès

Este ítem está publicado en

2013-06-20, Cancun. 2013-06-21

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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.< Leer menos

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Computational intelligence for cloud management: current trends and opportunities

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