Presenting and demonstrating new algorithm for optimum resource allocation in cloud computing based on Kalman filtering
Mots-clés :
Cloud Computing, Kalman Filter, Resource Allocation.Résumé
In recent decades lots of attention attracted toward Cloud Computing, and that’s only because of services and powers which present by cloud providers. They present different kind of services, such as: processing ability, storage capacity, Platform as a Service, Software as a Service. According to experts and managers of different industry opinions, two main difficulties in front of organizations and companies in order to migrate to cloud computing is security and accessible resources. In this paper we want to introduce new allocation algorithm for cloud resources. This algorithm is smart algorithm, which is based on Kalman filtering and allocates resources and powers to the users based on their usage backgrounds and their present demand.
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Références
Bruce, Berriman, G., Deelman, E., Groth, P., Juve G. (2011). The application of cloud computing to the creation of image mosaics and management of their provenance.
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.
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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Publiée
2018-12-27
Comment citer
Khosravi, S. (2018). Presenting and demonstrating new algorithm for optimum resource allocation in cloud computing based on Kalman filtering. Amazonia Investiga, 7(17), 200–210. Consulté à l’adresse https://amazoniainvestiga.info/index.php/amazonia/article/view/344
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