Optimized Cloud Based Scheduling

Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu (Autoren)

Buch | Hardcover

XIII, 99 Seiten

2018
Springer International Publishing (Verlag)
978-3-319-73212-1 (ISBN)

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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.

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

Erscheinungsdatum	21.03.2018
Reihe/Serie	Data, Semantics and Cloud Computing
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XIII, 99 p. 33 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	302 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Informatik ► Web / Internet
	Technik
Schlagworte	Big Data • Big Data Analytics • Cloud Based Scheduling • Cloud Computing • Cloud Service Provisioning • High Performance Computing • Hybrid Cloud
ISBN-10	3-319-73212-9 / 3319732129
ISBN-13	978-3-319-73212-1 / 9783319732121
Zustand	Neuware