@conference {7338079,
	title = {A software tool to efficiently manage the energy consumption of HPC clusters},
	booktitle = {2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2015},
	month = {Aug},
	pages = {1-8},
	abstract = {Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In this work we propose a software, named EECluster, to reduce the high energy consumption of HPC clusters. EECluster works with both OGE/SGE and PBS/TORQUE resource management systems and automatically tunes its decision-making mechanism based on a machine learning approach. The quality of the obtained results using this software are evaluated by means of experiments made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects.},
	keywords = {academic-cluster, Computational modeling, Computer architecture, Decision making, decision-making mechanism, EECluster software tool, Fuzzy systems, Genetics, high performance computing clusters, HPC cluster energy consumption, learning (artificial intelligence), machine learning approach, OGE-SGE resource management systems, Oviedo University, parallel processing, PBS-TORQUE resource management systems, power aware computing, resource allocation, Scientific Modelling Cluster, software tools},
	doi = {10.1109/FUZZ-IEEE.2015.7338079},
	author = {A. Coca{\~n}a-Fern{\'a}ndez and L. S{\'a}nchez and J. 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}
}
