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Optimized cloud based scheduling

Tan, Rong Kun Jason.

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Optimized cloud based scheduling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimized cloud based scheduling/ by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.
Author:	Tan, Rong Kun Jason.
other author:	Leong, John A.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xiii, 99 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
Contained By:	Springer eBooks
Subject:	Cloud computing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-73214-5
ISBN:	9783319732145

Optimized cloud based scheduling
Tan, Rong Kun Jason.

Optimized cloud based scheduling[electronic resource] /by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - Cham :Springer International Publishing :2018. - xiii, 99 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7591860-949X ;. - Studies in computational intelligence ;v.759..

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.

ISBN: 9783319732145

Standard No.: 10.1007/978-3-319-73214-5doiSubjects--Topical Terms:

1016782
Cloud computing.

LC Class. No.: QA76.585

Dewey Class. No.: 004.6782

Optimized cloud based scheduling
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245 1 0 $a Optimized cloud based scheduling $h [electronic resource] / $c by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiii, 99 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.759
505 0 $a Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
520 $a 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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700 1 $a Leong, John A. $3 3301995
700 1 $a Sidhu, Amandeep S. $3 3301996
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