Person:
Henares Vilaboa, Kevin

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First Name
Kevin
Last Name
Henares Vilaboa
URI
https://hdl.handle.net/20.500.14352/87005
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
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2018 - 201912020 - 20222
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Now showing 1 - 3 of 3
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    Integrated system architecture for internet of things model-driven design with applications in medicina
    (2022) Henares Vilaboa, Kevin; Risco Martín, Jose Luis; Ayala Rodrigo, José Luis; Hermida Correa, Román
    Over the past few years, we have seen how processing and storage architectures become cheaper and more efficient, communication infrastructures become faster and more scalable, and many new ways of interacting with the world around us are being developed. Every day more devices are connected to the network, and the generation of data worldwide is growing exponentially. In this context, the Internet of Things promises to be the new technological revolution, as was the introduction of the network of networks or universal mobile accessibility in tis day...
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    A unified cloud-enabled discrete event parallel and distributed simulation architecture
    (Simulation modelling practice and theory, 2022) Risco Martín, José Luis; Henares Vilaboa, Kevin; Mittal, Saurabh; Almendras Aruzamen, Luis Fernando; Olcoz Herrero, Katzalin
    Cloud infrastructure provides rapid resource provision for on-demand computational require-ments. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M & S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M & S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M & S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M & S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M & S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95x and 1.84x, respectively.
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    Sistema avanzado de predicción de crisis migrañosas
    (2018) Henares Vilaboa, Kevin; Risco Martín, José Luis; Pagán Ortiz, Josué
    La migraña es una de las enfermedades neurológicas más incapacitantes y está presente en alrededor del 10 % de la población mundial. Sin embargo, aunque se han hecho avances en cuanto a tratamientos de prevención, aún no se ha conseguido crear una cura para esta enfermedad. Por otra parte, la medicación existente para neutralizar episodios de migraña presenta una característica destacable: es notablemente más efectiva cuando se toma al inicio del episodio. Por el contrario, si se administra cuando ya haya comenzado el dolor su efecto queda reducido (o incluso anulado). No obstante, la mayoría de pacientes de migraña no son capaces de predecir cuando va a comenzar el dolor en un episodio de migraña, por lo que suelen realizar un uso no óptimo de la medicación asociada. Este proyecto presenta un sistema capaz de predecir la fases de dolor en episodios de migraña. Para ello, se recogen datos de pacientes y se crean modelos predictivos que permiten estimar la probabilidad de aparición de una nueva etapa de dolor. Estas predicciones permiten la generación de alarmas con una antelación de hasta 45 minutos. Por tanto, posibilita un uso adecuado de los medicamentos y aumenta la eficacia de los mismos.