Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 759)
Part of the book sub series: Data, Semantics and Cloud Computing (DSCC)
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Table of contents (7 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Optimized Cloud Based Scheduling
Authors: Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-73214-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-73212-1Published: 05 March 2018
Softcover ISBN: 978-3-030-10333-0Published: 11 February 2019
eBook ISBN: 978-3-319-73214-5Published: 24 February 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIII, 99
Number of Illustrations: 33 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Information Systems Applications (incl. Internet)