Optimized Cloud Based Scheduling

Rong Kun Jason Tan

ISBN 10: 3319732129 / ISBN 13: 9783319732121

Published by Springer, Berlin, Springer International Publishing, Springer, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319732121

Bibliographic Details

• Title: Optimized Cloud Based Scheduling
• Publisher: Springer, Berlin, Springer International Publishing, Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3319732129
• ISBN 13: 9783319732121
• Binding: Buch
• Condition: Neu
• Dimensions: 244W x 162H x 12D millimeters 244 millimeters width by 162 millimeters height by 12 millimeters depth
• Weight: 291 grams

About this title

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.