Optimized cloud based scheduling 1st ed.

Author

• Tan, Rong Kun Jason

Additional Author(s)

• Leong, John A.
• Sidhu, Amandeep S.

Publisher

Cham, Switzerland : Springer International Publishing, 2018

Language

English

ISBN

9783319732145

Series

Data, Semantics and Cloud Computing 759

Subject(s)

• APPLICATION SOFTWARE
• ARTIFICIAL INTELLIGENCE
• COMPUTATIONAL INTELLIGENCE

Notes

. .

Abstract

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.

Physical Dimension

Number of Page(s)

1 online resource (xIIi, 99 p.)

Dimension

-

Other Desc.

ill.

Summary / Review / Table of Content

Introduction --
Background --
Benchmarking --
Computation of Large Datasets --
Optimized Online Scheduling Algorithms --
Performance Evaluation --
Conclusion and Future Works --

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	00573/20	004.6782 Tan O	Online !	Available