Optimized Cloud Based Scheduling (Hardcover, 1st ed. 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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Data, Semantics and Cloud Computing, 759
Release date:	March 2018
First published:	2018
Authors:	Rong Kun Jason Tan • John A. Leong • Amandeep S. Sidhu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	99
Edition:	1st ed. 2018
ISBN-13:	978-3-319-73212-1
Categories:	Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-73212-9
Barcode:	9783319732121

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