Optimized Cloud Based Scheduling (Data, Semantics and Cloud Computing, Band 759) - Softcover

Buch 274 von 538: Studies in Computational Intelligence

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

 
9783030103330: Optimized Cloud Based Scheduling (Data, Semantics and Cloud Computing, Band 759)
Softcover
ISBN 10: 3030103331 ISBN 13: 9783030103330
Verlag: Springer, 2019

Inhaltsangabe

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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Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2019
Sprache
Englisch
ISBN 10 
3030103331
ISBN 13 
9783030103330
Einband
Taschenbuch
Anzahl der Seiten
116
Kontakt zum Hersteller
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Deutschland

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