Alberto Cocaña-Fernández

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Decision-making mechanisms for online allocation of computer node slots in HPC clusters are commonly based on simple knowledge-based systems comprised of individual sets of if–then rules. In contrast with previous works where these rules were designed using expert knowledge, two different types of evolutionary learning algorithms are compared in this paper.(More)
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(More)
In this work, a classifier that jointly optimises the expected total classification cost and the energy consumption is presented. A numerical study is provided, where different alternatives are implemented on a hearing aid. Our proposal is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of(More)
As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these(More)
NnmfPack is a library for the nonnegative matrix factorization (NNMF) problem. Nowadays NNMF is an essential tool in many fields spanning machine learning, data analysis, image analysis or audio source separation, among others. NnmfPack is an efficient numerical library conceived for shared memory heterogeneous parallel systems, and it supports, from its(More)
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