Optimized Cloud Based Scheduling

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.

List of contents

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

Summary

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.

Product details

• Authors: John Leong, John A Leong, John A. Leong, Amandeep Sidhu, Amandeep S. Sidhu, Rong Kun Jaso Tan, Rong Kun Jason Tan
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2019
• EAN: 9783030103330
• ISBN: 978-3-0-3010333-0
• No. of pages: 99
• Dimensions: 155 mm x 235 mm x 6 mm
• Weight: 190 g
• Illustrations: XIII, 99 p. 33 illus.
• Series: Studies in Computational Intelligence; Data, Semantics and Cloud Computing; Studies in Computational Intelligence; Data, Semantics and Cloud Computing
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; B, Artificial Intelligence, angewandte informatik, Information Retrieval, engineering, Information Systems Applications (incl. Internet), Information Systems Applications (incl.Internet), Computer and Information Systems Applications, Application software, Computational Intelligence, Internet searching