@article {COCANAFERNANDEZ201695,
	title = {Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {48},
	year = {2016},
	pages = {95 - 105},
	abstract = {A proactive mechanism to learn an efficient strategy for adaptive resource clusters is proposed. In contrast to reactive techniques, that rescale the cluster to fit the past load, a predictive strategy is adopted. The cluster incoming workload is forecasted and an optimization problem is defined whose solution is the optimal action according to a utility function. Genetic-based machine learning techniques are used, including multi-objective evolutionary algorithms under the distal supervised learning setup. Experimental evaluations show that the proactive system presented in this work improves either the energetic efficiency or the number of reconfigurations of previous approaches without a loss in the quality of service. Depending on the predictability of the workload, in real world cluster scenarios additional energy savings of up to approximately 40\% were measured over the best previous approach, with a 2{\texttimes} factor increment in the number of reconfigurations.},
	keywords = {Distal learning, Energy-efficient cluster computing, Evolutionary algorithms, Multi-criteria decision making},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615002183},
	author = {Alberto Coca{\~n}a-Fern{\'a}ndez and Luciano S{\'a}nchez and Jos{\'e} Ranilla}
}
@conference {6622418,
	title = {CI-LQD: A software tool for modeling and decision making with Low Quality Data},
	booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	month = {July},
	pages = {1-8},
	abstract = {The software tool CI-LQD (Computational Intelligence for Low Quality Data) is introduced in this paper. CI-LQD is an ongoing project that includes a lightweight open source software that has been designed with scientific and teaching purposes in mind. The main usefulness of the software is to automate the calculations involved in the statistical comparisons of different algorithms, with both numerical and graphical techniques, when the available information is interval-valued, fuzzy, incomplete or otherwise vague. A growing catalog of evolutionary algorithms for learning classifiers, models and association rules, along with their corresponding data conditioning and preprocessing techniques, is included. A demonstrative example of the tool is described that illustrates the capabilities of the software.},
	keywords = {Algorithm design and analysis, Artificial intelligence, Association rules, CI-LQD, Computational intelligence, data conditioning, data mining, data preprocessing technique, Databases, Decision making, evolutionary algorithm, Evolutionary algorithms, evolutionary computation, graphical technique, learning classifier, lightweight open source software, Low Quality Data, Probability distribution, Software algorithms, software tool, software tools, statistical analysis, statistical comparison},
	issn = {1098-7584},
	doi = {10.1109/FUZZ-IEEE.2013.6622418},
	author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
