Fog Asiste Cloud Paradigma para la Accesibilidad y Colaboración al Análisis de Datos Genómicos
DOI:
https://doi.org/10.37135/unach.ns.001.02.08Palabras clave:
Macrodatos, Administración de recursos distribuidos, Cloud paradigma, Fog paradigma, Secuenciaciones de nueva generación (NGS)Resumen
La secuenciación de la próxima generación es cada vez más creciente y requiere recursos informáticos a gran escala para manejar la enorme cantidad de datos producidos. El paradigma Cloud computing fácilmente maneja datos enormes, pero el problema central con este paradigma es la transferencia de datos enormes hacia y desde las computadoras en cloud debido al ancho de banda limitado que radica en la naturaleza centralizada de la arquitectura Cloud computing la cual está localizada lejos de los usuarios. Una arquitectura donde la potencia de computación se distribuya de manera más uniforme en toda la red es una forma de combatir este problema. La arquitectura debe llevar la capacidad de procesamiento hacia el borde de la red, más cerca de la fuente de los datos. Para esta propuesta, Fog computing ofrece una solución prometedora para acercar las capacidades computacionales a los datos generados y será la solución para ganar fuerza en la investigación genómica. Proponemos un nuevo modelo llamado Collaborative-Fog (Co-Fog) que adopta los paradigmas Fog y Cloud computing para administrar grandes conjuntos de datos genómicos y para permitir la comprensión de cómo las partes interesadas pueden gestionar la interacción y la colaboración. El presente trabajo describe el modelo Co-Fog que promete un mayor rendimiento, eficiencia energética, menor latencia, tiempo de respuesta más rápido, escalabilidad y una mejor precisión localizada para futuras colaboraciones a gran escala en la genómica.
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Referencias
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- Ruiter, J. and Warnier, M. (2011). Privacy regulations for cloud computing: Compliance and implementation in theory and practice. In Computers, privacy and data protection: an element of choice, pp. 361-376. Springer.
- Saharan, K. and Kumar, A. (2015). Fog in comparison to cloud: A survey. International Journal of Computer Applications, 122(3).
- Shanker, A. (2012). Genome research in the cloud. Omics: a journal of integrative biology, 16 (7-8), 422-428.
- Soltesz, S., Potzl, H., Fiuczynski, M. E., Bavier, A., and Peterson, L. Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In ACM SIGOPS Operating Systems Review, pp. 275-287. ACM.
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- Xu, X., Yu, H., and Pei, X. A novel resource scheduling approach in container based clouds. In Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on, pp. 257-264. IEEE.
- Zhang, Q., Liu, L., Pu, C., Dou, Q., Wu, L., and Zhou, W. (2018). A comparative study of containers and virtual machines in big data environment. arXiv preprint arXiv:1807.01842.
- Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
- Baccarelli, E., Naranjo, P. G. V., Scarpiniti, M., Shojafar, M., and Abawajy, J. H. (2017). Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE access, 5, 9882-9910.
- Baccarelli, E., Scarpiniti, M., Naranjo, P. G. V., and Vaca-Cardenas, L. (2018). Fog of social iot: When the fog becomes social. IEEE Network, 32(4), 68-80.
- Bernstein, D. (2014). Containers and cloud: From lxc to docker to kubernetes. IEEE Cloud Computing, 1(3), 81-84.
- Bonomi, F. Connected vehicles, the internet of things, and fog computing. In The Eighth ACM International Workshop on Vehicular Inter-Networking (VANET), Las Vegas, USA, pp. 13-15.
- Bonomi, F., Milito, R., Natarajan, P., and Zhu, J. (2014). Fog computing: A platform for internet of things and analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, pp. 169-186. Springer.
- Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16. ACM.
- Botta, A., De Donato, W., Persico, V., and Pescapé, A. (2016). Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems, 56, 684-700.
- Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., and Brandic, I. (2009). Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
- Byers, C. C. and Wetterwald, P. (2015). Fog computing distributing data and intelligence for resiliency and scale necessary for {IoT}: The internet of things (ubiquity symposium). Ubiquity, 2015 (November), 4.
- Charlebois, K., Palmour, N., and Knoppers, B. M. (2016). The adoption of cloud computing in the field of genomics research: the influence of ethical and legal issues. PloS one, 11 (10), e0164347.
- DNAnexus (2018). A global network for genomics. https://www.dnanexus.com/company.
- Dove, E. S., Joly, Y., Tassé, A.-M., in Genomics, P. P. P., Committee, S. P. I. S., Burton, P., Chisholm, R., Fortier, I., Goodwin, P., Harris, J., et al. (2015). Genomic cloud computing: legal and ethical points to consider. European Journal of Human Genetics, 23(10), 1271.
- ENCODE (2018). Encyclopedia of dna elements. https://www.encodeproject.org/.
- Firdhous, M., Ghazali, O., and Hassan, S. Fog computing: Will it be the future of cloud computing? In The Third International Conference on Informatics & Applications (ICIA2014).
- Forer, L., Schönnherr, S., Weißensteiner, H., Specht, G., Kronenberg, F., and Kloss-Brandst, A. (2012). Cloud computing computational medicine. Computational Medicine.
- Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., and Stoica, I. (2009). Above the clouds: A berkeley view of cloud computing. Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS, 28(13), 2009.
- Genomics, E. (2018). Data driven innovation. https://www.eaglegenomics.com/tag/aws/.
- Group, N. P. (2017). The clinical cancer genome task team of the global alliance for genomics and health. sharing clinical and genomic data on cancer - the need for global solutions. N. Engl. J. Med., 376, 2006-2009.
- Horne, J. (2018). Social implications of big data and fog computing. International Journal of Fog Computing (IJFC), 1(2), 1-50.
- ICGC (2018). International cancer genome consortium. https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program.
- Kai, K., Cong, W., and Tao, L. (2016). Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. The Journal of China Universities of Posts and Telecommunications, 23(2), 56-96.
- Langmead, B. and Nellore, A. (2018). Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics, 19 (4), 208.
- Luan, T. H., Gao, L., Li, Z., Xiang, Y., and Sun, L. (2015). Fog computing: Focusing on mobile users at the edge, arXiv preprint arXiv:1502.01815.
- Madsen, H., Burtschy, B., Albeanu, G., and Popentiu-Vladicescu, F. Reliability in the utility computing era: Towards reliable fog computing. In Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on, pp. 43-46. IEEE.
- Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing. NIST.
- modENCODE (2018). The national human genome research institute model organism encyclopedia of dna elements. http://www.modencode.org/.
- Naranjo, P. G. V., Shojafar, M., Vaca-Cardenas, L., Canali, C., Lancellotti, R., and Baccarelli, E. Big data over smartgrid — a fog computing perspective. In Proceedings of the 24th International Conference on Software, Telecommunications and Computer Networks, pp. 22-24. SoftCOM 2016. NCBI (2018). Sequence read archive. https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi
- Nellore, A., Jaffe, A. E., Fortin, J.-P., Alquicira-Hernández, J., Collado-Torres, L., Wang, S., Phillips III, R. A., Karbhari, N., Hansen, K. D., Langmead, B., et al. (2016). Human splicing diversity and the extent of unannotated splice junctions across human rna-seq samples on the sequence read archive. Genome biology, 17 (1), 266.
- NIH (2014).Trans-omics for precision medicine (topmed) program. https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program.
- NIST (2018a). Definition of cloud computing. https://csrc.nist.gov/publications/detail/sp/800-145/final.
- NIST (2018b). Fog computing conceptual model. https://www.nist.gov/news-events/news/2018/03/nist-releases-special-publication-500-325\- fog-computing-conceptual-model.
- Portnoy, M. (2012). Virtualization essentials, volume 19. John Wiley & Sons.
- Ruiter, J. and Warnier, M. (2011). Privacy regulations for cloud computing: Compliance and implementation in theory and practice. In Computers, privacy and data protection: an element of choice, pp. 361-376. Springer.
- Saharan, K. and Kumar, A. (2015). Fog in comparison to cloud: A survey. International Journal of Computer Applications, 122(3).
- Shanker, A. (2012). Genome research in the cloud. Omics: a journal of integrative biology, 16 (7-8), 422-428.
- Soltesz, S., Potzl, H., Fiuczynski, M. E., Bavier, A., and Peterson, L. Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In ACM SIGOPS Operating Systems Review, pp. 275-287. ACM.
- Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., and Yang, Q. (2017). Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Transactions on Industrial informatics, 13(5), 2140-2150.
- Xu, X., Yu, H., and Pei, X. A novel resource scheduling approach in container based clouds. In Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on, pp. 257-264. IEEE.
- Zhang, Q., Liu, L., Pu, C., Dou, Q., Wu, L., and Zhou, W. (2018). A comparative study of containers and virtual machines in big data environment. arXiv preprint arXiv:1807.01842.
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Publicado
2018-12-12
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Artículos de Investigación y Artículos de Revisión
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Fog Asiste Cloud Paradigma para la Accesibilidad y Colaboración al Análisis de Datos Genómicos. (2018). Novasinergia, ISSN 2631-2654, 1(2), 70-82. https://doi.org/10.37135/unach.ns.001.02.08
