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Accelerating smart eHealth services execution at the fog computing infrastructure

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Accelerating smart eHealth services execution at the fog computing infrastructure

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dc.contributor.author Garcia Valls, Marisol es_ES
dc.contributor.author Calva-Urrego, Christian es_ES
dc.contributor.author García-Fornes, A. es_ES
dc.date.accessioned 2021-03-13T04:30:48Z
dc.date.available 2021-03-13T04:30:48Z
dc.date.issued 2020-07 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/163814
dc.description.abstract [EN] Fog computing improves the execution of computationally intensive services for remote client nodes as part of the data processing is performed close to the location where the results will be delivered. As opposed to other services running on smart cities, a major challenge of eHealth services on the fog is that they typically span multiple computational activities performing big data processing over sensible information that must be protected. Using the capacities of current processors can improve the servicing of remote patient nodes. This paper presents the design and validation of a framework that improves the service time of selected activities at the fog servers; precisely, of those activities requested by remote patients. It exploits the capacities of current processors to parallelize specific activities that can be run on reserved cores, and it relies on the quality of service guarantees of data distribution platforms to improve communication and response times to mobile patients. The proposed approach is validated on a prototype implementation of simulated computationally-intensive eHealth interactions, decreasing the response time by 4x when core reservation is activated. (C) 2018 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship This work has been primarily funded by the M2C2 (TIN201456158-C4-3-P) and PRECON-I4 (TIN2017-86520-C3-2-R), both funded by the Spanish Ministry of Economy and Competitiveness, Spain. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Fog computing es_ES
dc.subject Resource management es_ES
dc.subject Multicore Distribution software es_ES
dc.subject Quality of service es_ES
dc.subject EHealth services es_ES
dc.subject Computation intensive services es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Accelerating smart eHealth services execution at the fog computing infrastructure es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2018.07.001 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86520-C3-2-R/ES/SISTEMAS INFORMATICOS PREDECIBLES Y CONFIABLES PARA LA INDUSTRIA 4.0/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2011-28339/ES/DESARROLLO DE MIDDLEWARE PARA LA RECONFIGURACION EN TIEMPO REAL DE SISTEMAS DISTRIBUIDOS DE VIDEO VIGILANCIA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2014-56158-C4-3-P/ES/SISTEMAS CIBER-FISICOS DE CRITICIDAD MIXTA SOBRE PLATAFORMAS MULTINUCLEO/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Garcia Valls, M.; Calva-Urrego, C.; García-Fornes, A. (2020). Accelerating smart eHealth services execution at the fog computing infrastructure. Future Generation Computer Systems. 108:882-893. https://doi.org/10.1016/j.future.2018.07.001 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2018.07.001 es_ES
dc.description.upvformatpinicio 882 es_ES
dc.description.upvformatpfin 893 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 108 es_ES
dc.relation.pasarela S\369065 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.description.references García-Valls, M., Cucinotta, T., & Lu, C. (2014). Challenges in real-time virtualization and predictable cloud computing. Journal of Systems Architecture, 60(9), 726-740. doi:10.1016/j.sysarc.2014.07.004 es_ES
dc.description.references García-Valls, M., Dubey, A., & Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture, 91, 83-102. doi:10.1016/j.sysarc.2018.05.007 es_ES
dc.description.references Bahtovski, A., & Gusev, M. (2014). Cloudlet Challenges. Procedia Engineering, 69, 704-711. doi:10.1016/j.proeng.2014.03.045 es_ES
dc.description.references Eze, B., Kuziemsky, C., & Peyton, L. (2017). Cloud-based performance management of community care services. Journal of Software: Evolution and Process, 30(7), e1897. doi:10.1002/smr.1897 es_ES
dc.description.references Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017). Advanced internet of things for personalised healthcare systems: A survey. Pervasive and Mobile Computing, 41, 132-149. doi:10.1016/j.pmcj.2017.06.018 es_ES
dc.description.references Ahmed, S. H., & Rani, S. (2018). A hybrid approach, Smart Street use case and future aspects for Internet of Things in smart cities. Future Generation Computer Systems, 79, 941-951. doi:10.1016/j.future.2017.08.054 es_ES
dc.description.references Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117-128. doi:10.1016/j.measurement.2018.01.022 es_ES
dc.description.references Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017). Security and Privacy in Fog Computing: Challenges. IEEE Access, 5, 19293-19304. doi:10.1109/access.2017.2749422 es_ES
dc.description.references Elhoseny, M., Ramirez-Gonzalez, G., Abu-Elnasr, O. M., Shawkat, S. A., Arunkumar, N., & Farouk, A. (2018). Secure Medical Data Transmission Model for IoT-Based Healthcare Systems. IEEE Access, 6, 20596-20608. doi:10.1109/access.2018.2817615 es_ES
dc.description.references Jadhav, A., Andrews, D., Fiksdal, A., Kumbamu, A., McCormick, J. B., Misitano, A., … Pathak, J. (2014). Comparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal. Journal of Medical Internet Research, 16(7), e160. doi:10.2196/jmir.3186 es_ES
dc.description.references Golov, N., & Rönnbäck, L. (2017). Big Data normalization for massively parallel processing databases. Computer Standards & Interfaces, 54, 86-93. doi:10.1016/j.csi.2017.01.009 es_ES
dc.description.references Shehab, A., Elhoseny, M., Muhammad, K., Sangaiah, A. K., Yang, P., Huang, H., & Hou, G. (2018). Secure and Robust Fragile Watermarking Scheme for Medical Images. IEEE Access, 6, 10269-10278. doi:10.1109/access.2018.2799240 es_ES
dc.description.references Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394. doi:10.1016/j.future.2018.03.005 es_ES
dc.description.references Garcia Valls, M., Lopez, I. R., & Villar, L. F. (2013). iLAND: An Enhanced Middleware for Real-Time Reconfiguration of Service Oriented Distributed Real-Time Systems. IEEE Transactions on Industrial Informatics, 9(1), 228-236. doi:10.1109/tii.2012.2198662 es_ES
dc.description.references García-Valls, M., Perez-Palacin, D., & Mirandola, R. (2018). Pragmatic cyber physical systems design based on parametric models. Journal of Systems and Software, 144, 559-572. doi:10.1016/j.jss.2018.06.044 es_ES
dc.description.references The OpenMP® API specification for parallel programming. http://www.openmp.org/ (Accessed June 2017). es_ES
dc.description.references Message Passing Interface Forum. http://www.mpi-forum.org/ (Accessed June 2017). es_ES
dc.description.references Kuhn, B., Petersen, P., & O’Toole, E. (2000). OpenMP versus threading in C/C++. Concurrency: Practice and Experience, 12(12), 1165-1176. doi:10.1002/1096-9128(200010)12:12<1165::aid-cpe529>3.0.co;2-l es_ES
dc.description.references MPI Intel, Benchmarks: Users Guide and Methodology Description, Intel GmbH, Germany. es_ES
dc.description.references Object Management Group, A data distribution service for real-time systems version 1.4, 2015. http://www.omg.org/spec/DDS/1.4. es_ES
dc.description.references Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007 es_ES
dc.description.references Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540-1557. doi:10.1016/j.jss.2012.01.045 es_ES
dc.description.references Burdalo, L., Terrasa, A., Espinosa, A., & Garcia-Fornes, A. (2012). Analyzing the Effect of Gain Time on Soft-Task Scheduling Policies in Real-Time Systems. IEEE Transactions on Software Engineering, 38(6), 1305-1318. doi:10.1109/tse.2011.95 es_ES


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