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Cloud broker and cloudlet for workfl...

Chen, Min.

Cloud broker and cloudlet for workflow scheduling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cloud broker and cloudlet for workflow schedulingby Chan-Hyun Youn, Min Chen, Patrizio Dazzi.
作者:	Youn, Chan-Hyun.
其他作者:	Chen, Min.
出版者:	Singapore :Springer Singapore :2017.
面頁冊數:	ix, 212 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Cloud computingEnergy consumption.
電子資源:	http://dx.doi.org/10.1007/978-981-10-5071-8
ISBN:	9789811050718$q(electronic bk.)

Cloud broker and cloudlet for workflow scheduling
Youn, Chan-Hyun.

Cloud broker and cloudlet for workflow scheduling[electronic resource] /by Chan-Hyun Youn, Min Chen, Patrizio Dazzi. - Singapore :Springer Singapore :2017. - ix, 212 p. :ill., digital ;24 cm. - KAIST research series,2214-2541. - KAIST research series..

1 Integrated Cloud Broker System and Its Experimental Evaluation -- 2 VM Placement via Resource Brokers in a Cloud Datacenter -- 3 Cost Adaptive Workflow Resource Broker in Cloud -- 4 A Cloud Broker System for Connected Car Services with an Integrated Simulation Framework -- 5 Mobile Device as Cloud Broker for Computation Offloading at Cloudlets -- 6 Opportunistic Task Scheduling over Co-Located Clouds -- 7 Mobility-Aware Resource Scheduling Cloudlets in Mobile Environment -- 8 Machine-learning based approaches for cloud brokering.

This book blends the principles of cloud computing theory and discussion of emerging technologies in cloud broker systems, enabling users to realise the potential of an integrated broker system for scientific applications and the Internet of Things (IoT) Due to dynamic situations in user demand and cloud resource status, scalability has become crucial in the execution of complex scientific applications. Therefore, data analysts and computer scientists must grasp workflow management issues in order to better understand the characteristics of cloud resources, allocate these resources more efficiently and make critical decisions intelligently. Thus, this book addresses these issues through discussion of some novel approaches and engineering issues in cloud broker systems and cloudlets for workflow scheduling. This book closes the gaps between cloud programmers and scientific applications designers, describing the fundamentals of cloud broker system technology and the state-of-the-art applications in implementation and performance evaluation. The books gives details of scheduling structures and processes, providing guidance and inspiration for users including cloud programmers, application designers and decision makers with involvement in cloud resource management.

ISBN: 9789811050718$q(electronic bk.)

Standard No.: 10.1007/978-981-10-5071-8doiSubjects--Topical Terms:

787325
Cloud computing
--Energy consumption.

LC Class. No.: QA76.585

Dewey Class. No.: 004.6782

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520 $a This book blends the principles of cloud computing theory and discussion of emerging technologies in cloud broker systems, enabling users to realise the potential of an integrated broker system for scientific applications and the Internet of Things (IoT) Due to dynamic situations in user demand and cloud resource status, scalability has become crucial in the execution of complex scientific applications. Therefore, data analysts and computer scientists must grasp workflow management issues in order to better understand the characteristics of cloud resources, allocate these resources more efficiently and make critical decisions intelligently. Thus, this book addresses these issues through discussion of some novel approaches and engineering issues in cloud broker systems and cloudlets for workflow scheduling. This book closes the gaps between cloud programmers and scientific applications designers, describing the fundamentals of cloud broker system technology and the state-of-the-art applications in implementation and performance evaluation. The books gives details of scheduling structures and processes, providing guidance and inspiration for users including cloud programmers, application designers and decision makers with involvement in cloud resource management.
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