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Optimized Cloud Based Scheduling (Studies in Computational Intelligence, nr. 759)

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en Limba Engleză Hardback – 05 Mar 2018
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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Specificații

ISBN-13: 9783319732121
ISBN-10: 3319732129
Pagini: 200
Ilustrații: XIII, 99 p. 33 illus.
Dimensiuni: 155 x 235 mm
Greutate: 3.02 kg
Ediția: 1st ed. 2018
Editura: Springer
Colecția Springer
Seriile Studies in Computational Intelligence , Data, Semantics and Cloud Computing

Locul publicării: Cham, Switzerland

Cuprins

Introduction.- Background.- Benchmarking.- Computation of Large Datasets.- Optimized Online Scheduling Algorithms.

Textul de pe ultima copertă

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.

Caracteristici

Presents an improved design for service provisioning and allocation models in a hybrid cloud environment
Proposes approaches for addressing scheduling and performance issues in big data analytics
Showcases new algorithms for hybrid cloud scheduling