Optimized Cloud Based Scheduling

✍ Scribed by Rong Kun Jason Tan,John A. Leong,Amandeep S. Sidhu (auth.)

Publisher: Springer International Publishing
Year: 2018
Tongue: English
Leaves: 106
Series: Data, Semantics and Cloud Computing 759
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiii
Introduction (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 1-9
Background (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 11-33
Benchmarking (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 35-45
Computation of Large Datasets (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 47-64
Optimized Online Scheduling Algorithms (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 65-81
Performance Evaluation (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 83-89
Conclusion and Future Works (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 91-92
Back Matter ....Pages 93-99

✦ Subjects

Computational Intelligence

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