Optimized Cloud Based Scheduling [electronic resource] / by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.

Por: Tan, Rong Kun Jason [author.]Colaborador(es): Leong, John A [author.] | Sidhu, Amandeep S [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Data, Semantics and Cloud Computing ; 759Editor: Cham : Springer International Publishing : Imprint: Springer, 2018Edición: 1st ed. 2018Descripción: XIII, 99 p. 33 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319732145Tema(s): Computational intelligence | Artificial intelligence | Application software | Computational Intelligence | Artificial Intelligence | Information Systems Applications (incl. Internet)Formatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
En: Springer Nature eBookResumen: This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
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Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.

UABC ; Temporal ; 01/01/2021-12/31/2023.

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