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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 |