Optimized Cloud Based Scheduling: 759 (Data, Semantics and Cloud Computing) - Tapa blanda

Libro 274 de 538: Studies in Computational Intelligence

Tan, Rong Kun Jason; Leong, John A.; Sidhu, Amandeep S.

 
9783030103330: Optimized Cloud Based Scheduling: 759 (Data, Semantics and Cloud Computing)
Tapa blanda
ISBN 10: 3030103331 ISBN 13: 9783030103330
Editorial: Springer, 2019

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.

De la contraportada

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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Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030103331
ISBN 13 
9783030103330
Encuadernación
Tapa blanda
Número de páginas
116
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Alemania

Otras ediciones populares con el mismo título

9783319732121: Optimized Cloud Based Scheduling: 759 (Studies in Computational Intelligence, 759)

Edición Destacada

ISBN 10:  3319732129 ISBN 13:  9783319732121
Editorial: Springer, 2018
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