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)
  • ARTIFICIAL INTELLIGENCE
  • COMPUTATIONAL INTELLIGENCE
  • APPLICATION SOFTWARE
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
1.00573/20004.6782 Tan OOnline !Available

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