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Automated workflow scheduling in sel...

Kousalya, G.

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Automated workflow scheduling in self-adaptive clouds = concepts, algorithms and methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated workflow scheduling in self-adaptive clouds/ by G. Kousalya, P. Balakrishnan, C. Pethuru Raj.
Reminder of title:	concepts, algorithms and methods /
Author:	Kousalya, G.
other author:	Balakrishnan, P.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xvii, 225 p. :ill., digital ;24 cm.
[NT 15003449]:	Stepping into the Digital Intelligence Era -- Demystifying the Traits of Software-Defined Cloud Environments (SDCEs) -- Workflow Management Systems -- Workflow Scheduling Algorithms and Approaches -- Workflow Modeling and Simulation Techniques -- Execution of Workflow Scheduling in Cloud Middleware -- Workflow Predictions through Operational Analytics and Machine Learning -- Workflow Integration and Orchestration - Opportunities and the Challenges -- Workload Consolidation through Automated Workload Scheduling -- Automated Optimization Methods for Workflow Execution -- Hybrid IT: Characteristics and Capabilities.
Contained By:	Springer eBooks
Subject:	Workflow management systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-56982-6
ISBN:	9783319569826

Automated workflow scheduling in self-adaptive clouds = concepts, algorithms and methods /
Kousalya, G.

Automated workflow scheduling in self-adaptive cloudsconcepts, algorithms and methods /[electronic resource] :by G. Kousalya, P. Balakrishnan, C. Pethuru Raj. - Cham :Springer International Publishing :2017. - xvii, 225 p. :ill., digital ;24 cm. - Computer communications and networks,1617-7975. - Computer communications and networks..

Stepping into the Digital Intelligence Era -- Demystifying the Traits of Software-Defined Cloud Environments (SDCEs) -- Workflow Management Systems -- Workflow Scheduling Algorithms and Approaches -- Workflow Modeling and Simulation Techniques -- Execution of Workflow Scheduling in Cloud Middleware -- Workflow Predictions through Operational Analytics and Machine Learning -- Workflow Integration and Orchestration - Opportunities and the Challenges -- Workload Consolidation through Automated Workload Scheduling -- Automated Optimization Methods for Workflow Execution -- Hybrid IT: Characteristics and Capabilities.

This timely text/reference presents a comprehensive review of the workflow scheduling algorithms and approaches that are rapidly becoming essential for a range of software applications, due to their ability to efficiently leverage diverse and distributed cloud resources. Particular emphasis is placed on how workflow-based automation in software-defined cloud centers and hybrid IT systems can significantly enhance resource utilization and optimize energy efficiency. Topics and features: Describes dynamic workflow and task scheduling techniques that work across multiple (on-premise and off-premise) clouds Presents simulation-based case studies, and details of real-time test bed-based implementations Offers analyses and comparisons of a broad selection of static and dynamic workflow algorithms Examines the considerations for the main parameters in projects limited by budget and time constraints Covers workflow management systems, workflow modeling and simulation techniques, and machine learning approaches for predictive workflow analytics This must-read work provides invaluable practical insights from three subject matter experts in the cloud paradigm, which will empower IT practitioners and industry professionals in their daily assignments. Researchers and students interested in next-generation software-defined cloud environments will also greatly benefit from the material in the book. Dr. G. Kousalya is a Professor in the Department of Computer Science and Engineering at Coimbatore Institute of Technology, Coimbatore, India. Dr. P. Balakrishnan is an Associate Professor in the Department of Computer Science and Engineering at SASTRA University, Thanjavur, India. Dr. C. Pethuru Raj is the chief architect for Reliance Jio Cloud, Bangalore, India. His other publications include the Springer title High-Performance Big-Data Analytics.

ISBN: 9783319569826

Standard No.: 10.1007/978-3-319-56982-6doiSubjects--Topical Terms:

2145957
Workflow management systems.

LC Class. No.: HD62.17

Dewey Class. No.: 658.53

Automated workflow scheduling in self-adaptive clouds = concepts, algorithms and methods /
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245 1 0 $a Automated workflow scheduling in self-adaptive clouds $h [electronic resource] : $b concepts, algorithms and methods / $c by G. Kousalya, P. Balakrishnan, C. Pethuru Raj.
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505 0 $a Stepping into the Digital Intelligence Era -- Demystifying the Traits of Software-Defined Cloud Environments (SDCEs) -- Workflow Management Systems -- Workflow Scheduling Algorithms and Approaches -- Workflow Modeling and Simulation Techniques -- Execution of Workflow Scheduling in Cloud Middleware -- Workflow Predictions through Operational Analytics and Machine Learning -- Workflow Integration and Orchestration - Opportunities and the Challenges -- Workload Consolidation through Automated Workload Scheduling -- Automated Optimization Methods for Workflow Execution -- Hybrid IT: Characteristics and Capabilities.
520 $a This timely text/reference presents a comprehensive review of the workflow scheduling algorithms and approaches that are rapidly becoming essential for a range of software applications, due to their ability to efficiently leverage diverse and distributed cloud resources. Particular emphasis is placed on how workflow-based automation in software-defined cloud centers and hybrid IT systems can significantly enhance resource utilization and optimize energy efficiency. Topics and features: Describes dynamic workflow and task scheduling techniques that work across multiple (on-premise and off-premise) clouds Presents simulation-based case studies, and details of real-time test bed-based implementations Offers analyses and comparisons of a broad selection of static and dynamic workflow algorithms Examines the considerations for the main parameters in projects limited by budget and time constraints Covers workflow management systems, workflow modeling and simulation techniques, and machine learning approaches for predictive workflow analytics This must-read work provides invaluable practical insights from three subject matter experts in the cloud paradigm, which will empower IT practitioners and industry professionals in their daily assignments. Researchers and students interested in next-generation software-defined cloud environments will also greatly benefit from the material in the book. Dr. G. Kousalya is a Professor in the Department of Computer Science and Engineering at Coimbatore Institute of Technology, Coimbatore, India. Dr. P. Balakrishnan is an Associate Professor in the Department of Computer Science and Engineering at SASTRA University, Thanjavur, India. Dr. C. Pethuru Raj is the chief architect for Reliance Jio Cloud, Bangalore, India. His other publications include the Springer title High-Performance Big-Data Analytics.
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