Optimized Cloud Based Scheduling
Tan, Rong Kun Jason
Optimized Cloud Based Scheduling by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - 1 recurso en línea (XIII, 99 páginas 33 ilustraciones) - Data, Semantics and Cloud Computing, 759 2524-6593 ; Engineering (Springer-11647) .
Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
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.
9783319732145
10.1007/978-3-319-73214-5 doi
Informática en la nube
QA76.585 2018 EB
Optimized Cloud Based Scheduling by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - 1 recurso en línea (XIII, 99 páginas 33 ilustraciones) - Data, Semantics and Cloud Computing, 759 2524-6593 ; Engineering (Springer-11647) .
Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
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.
9783319732145
10.1007/978-3-319-73214-5 doi
Informática en la nube
QA76.585 2018 EB