Presentar y demostrar un nuevo algoritmo para la asignación óptima de recursos en la computación en la nube basada en el filtrado de Kalman
Palabras clave:
Computación en la nube, filtro de Kalman, asignación de recursos.Resumen
En las últimas décadas, se ha prestado mucha atención a la computación en la nube, y eso solo se debe a los servicios y poderes que ofrecen los proveedores de la nube. Presentan diferentes tipos de servicios, tales como: capacidad de procesamiento, capacidad de almacenamiento, plataforma como servicio, software como servicio. Según expertos y gerentes de diferentes opiniones de la industria, dos de las principales dificultades frente a las organizaciones y empresas para migrar a la computación en la nube son la seguridad y los recursos accesibles. En este documento queremos introducir un nuevo algoritmo de asignación para los recursos de la nube. Este algoritmo es un algoritmo inteligente, que se basa en el filtrado de Kalman y asigna recursos y poderes a los usuarios en función de sus antecedentes de uso y su demanda actual.
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Darbandi, M. (2017). Kalman Filtering for Estimation and Prediction Servers with Lower Traffic Loads for Transferring High-Level Processes in Cloud Computing; Published by HCTL International Journal of Technology Innovations and Research, (ISSN: 2321-1814), Vol. 23, Issue 1, pp. 10-20, Feb. DOI: 10.5281/Zenodo.345288.
Darbandi, M. (2017a). Proposing New Intelligence Algorithm for Suggesting Better Services to Cloud Users based on Kalman Filtering”; Published by Journal of Computer Sciences and Applications, Vol. 5, Issue 1, PP. 11-16.
Darbandi, M. (2017b). Proposing New Intelligent System for Suggesting Better Service Providers in Cloud Computing based on Kalman Filtering.
Published by HCTL International Journal of Technology Innovations and Research, Vol. 24, Issue 1, pp. 1-9.
Darbandi, M., Yousefi, S. (2017). Feasibility Study and Designing Smart Software for Control and Assessment of Human Health by means of Cloud Computing and Kalman Filtering [In Persian]; Published by International Journal of Researches in Science and Engineering, pp. 1-14.
Farahani Rad, A. (2013). Cloud Computing and its Implications for Organizational Design and Performance, MSC. Thesis, MIT University, USA.
Francis, L. (2009). Cloud Computing: Implications for Enterprise Software Vendors, MSC. Thesis, MIT University, USA.
Kashefi, F., Darbandi, M. (2011). Perusal about influences of Cloud Computing on the processes of these days and presenting new ideas about its security, International IEEE Conf. AICT., Dec. Baku,Azerbaijan.
Lundblad, J.J. (1974). Application of the Extended Kalman Filtering Technique to Ship Maneuvering Analysis, MSC. Thesis, MIT University, USA.
Madhavapeddy, A., Mortier, R., Crowcroft, J., Hand S. (2010). Multiscale not multi core: efficient heterogeneous cloud computing, published by the British Informatics Society Ltd. Proceedings of ACM-BCS Visions of Computer Science.
Menasce D.A., Ngo P. (2009). understanding cloud computing: experimentation and capacity planning. Proc. 2009, Computer Measurement Group Conf. Dallas, TX. Dec.
Buyya, R., Yeo, C.S., Venugopal, S., Broberg J., Brandic, I. (2012). cloud computing and Emerging IT platforms: Vision, Hype, and Reality for delivering computing as the 5th utility.
Campbell, R., Gupta, I., Heath, M., Ko, S.Y., Kozuch, M., Kunze, M., Kwan, T., Lai, K., Lee, H.Y., Lyons, M., Milojicic, D., O’Hallaron, D., Soh, Y.C. (2013). open cirrus TM cloud computing testbed: federated data centers for open source systems and services research,
Chard K., Caton, S., Rana, O., Bubendorfer, K. (2008). social cloud: cloud computing in social networks.
Cryans, J.D., April, A., Abran, A. (2008). criteria to compare cloud computing with current database technology, R. Dumke et al. (Eds.): IWSM / MetriKon / Mensura 2008, LNCS 5338, pp. 114-126.
Darbandi, M. (2017). Kalman Filtering for Estimation and Prediction Servers with Lower Traffic Loads for Transferring High-Level Processes in Cloud Computing; Published by HCTL International Journal of Technology Innovations and Research, (ISSN: 2321-1814), Vol. 23, Issue 1, pp. 10-20, Feb. DOI: 10.5281/Zenodo.345288.
Darbandi, M. (2017a). Proposing New Intelligence Algorithm for Suggesting Better Services to Cloud Users based on Kalman Filtering”; Published by Journal of Computer Sciences and Applications, Vol. 5, Issue 1, PP. 11-16.
Darbandi, M. (2017b). Proposing New Intelligent System for Suggesting Better Service Providers in Cloud Computing based on Kalman Filtering.
Published by HCTL International Journal of Technology Innovations and Research, Vol. 24, Issue 1, pp. 1-9.
Darbandi, M., Yousefi, S. (2017). Feasibility Study and Designing Smart Software for Control and Assessment of Human Health by means of Cloud Computing and Kalman Filtering [In Persian]; Published by International Journal of Researches in Science and Engineering, pp. 1-14.
Farahani Rad, A. (2013). Cloud Computing and its Implications for Organizational Design and Performance, MSC. Thesis, MIT University, USA.
Francis, L. (2009). Cloud Computing: Implications for Enterprise Software Vendors, MSC. Thesis, MIT University, USA.
Kashefi, F., Darbandi, M. (2011). Perusal about influences of Cloud Computing on the processes of these days and presenting new ideas about its security, International IEEE Conf. AICT., Dec. Baku,Azerbaijan.
Lundblad, J.J. (1974). Application of the Extended Kalman Filtering Technique to Ship Maneuvering Analysis, MSC. Thesis, MIT University, USA.
Madhavapeddy, A., Mortier, R., Crowcroft, J., Hand S. (2010). Multiscale not multi core: efficient heterogeneous cloud computing, published by the British Informatics Society Ltd. Proceedings of ACM-BCS Visions of Computer Science.
Menasce D.A., Ngo P. (2009). understanding cloud computing: experimentation and capacity planning. Proc. 2009, Computer Measurement Group Conf. Dallas, TX. Dec.
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Publicado
2018-12-27
Cómo citar
Khosravi, S. (2018). Presentar y demostrar un nuevo algoritmo para la asignación óptima de recursos en la computación en la nube basada en el filtrado de Kalman. Amazonia Investiga, 7(17), 200–210. Recuperado a partir de https://amazoniainvestiga.info/index.php/amazonia/article/view/344
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