Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associa...

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Detalles Bibliográficos
Autor: Awaysheh, Feras Mahmoud Naji
Tipo de recurso: tesis doctoral
Fecha de publicación:2020
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/23232
Acceso en línea:http://hdl.handle.net/10347/23232
Access Level:acceso abierto
Palabra clave:Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330499 Big Data
Descripción
Sumario:One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study.