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Author Kousalya, G.,
Title Automated workflow scheduling in self-adaptive clouds : concepts, algorithms and methods / G. Kousalya, P. Balakrishnan, C. Pethuru Raj.
Imprint Cham : Springer, 2017.

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Author Kousalya, G.,
Series Computer communications and networks
Computer communications and networks.
Subject Workflow management systems.
Cloud computing.
Adaptive control systems.
Alt Name Balakrishnan, P.,
Raj, C. Pethuru,
Description 1 online resource.
Contents Chapter 1: Stepping into the Digital Intelligence Era; 1.1 Introduction; 1.2 Elucidating Digitization Technologies; 1.2.1 Why Digitization?; 1.3 The Internet of Things (IoT)/Internet of Everything (IoE); 1.4 Real-Time, Predictive, and Prescriptive Analytics; 1.5 Envisioning the Digital Universe; 1.6 Describing the Digitization-Driven Big Data World; 1.7 The Cloud Infrastructures for the Digitization Era; 1.8 Integrated Platform for Big Data Analytics (Dr. Barry Devlin [1]); 1.9 Conclusion; References.
Chapter 2: Demystifying the Traits of Software-Defined Cloud Environments (SDCEs)2.1 Introduction; 2.2 Reflecting the Cloud Journey; 2.2.1 Elucidating the Cloudification Process; 2.2.2 The IT Commoditization and Compartmentalization; 2.3 Visualizing the Future; 2.4 The Emergence of Software-Defined Cloud Environments (SECEs); 2.5 The Major Building Blocks of Software-Defined Cloud Environments (SDCEs); 2.5.1 Network Virtualization; 2.5.2 Network Functions Virtualization (NFV); 2.5.3 Software-Defined Networking (SDN); 2.5.4 The Key Motivations for SDN; 2.5.5 The Need of SDN for the Cloud.
2.6 The Distinct Benefits of Software-Defined Networking2.7 Accentuating Software-Defined Storage (SDS); 2.8 The Key Characteristics of Software-Defined Storage (SDS); 2.8.1 Software-Defined Wide Area Networking (SD-WAN); 2.9 The Key Benefits of Software-Defined Cloud Environments (SDCEs); 2.10 Conclusion; References; Chapter 3: Workflow Management Systems; 3.1 Introduction; 3.2 Workflow Management System; 3.3 Kepler; 3.4 Taverna; 3.5 Triana; 3.6 Pegasus; 3.7 ASKALON; 3.8 Conclusion; References; Chapter 4: Workflow Scheduling Algorithms and Approaches; 4.1 Introduction; 4.2 Workflow Model.
4.3 Static Workflow Scheduling4.4 Dynamic Workflow Scheduling; 4.5 Workflow Scheduling; 4.6 Taxonomy of Cloud Resource Scheduling; 4.7 Existing Workflow Scheduling Algorithms; 4.7.1 Best Effort Workflow Scheduling; 4.7.2 Bi-objective Workflow Scheduling; 4.7.3 Multi-objective Workflow Scheduling; 4.8 Issues of Scheduling Workflow in Cloud; 4.9 Conclusion; References; Chapter 5: Workflow Modeling and Simulation Techniques; 5.1 Introduction; 5.2 Architecture of CloudSim; 5.3 Layered Design and Implementation of CloudSim Framework; 5.4 Experimental Results Using CloudSim; 5.5 WorkflowSim.
5.6 Architecture of WorkflowSim5.7 Conclusion; References; Chapter 6: Execution of Workflow Scheduling in Cloud Middleware; 6.1 Introduction; 6.2 Workflow Management System; 6.3 Experimental Setup; 6.4 General Steps for Submitting Workflow in Pegasus; 6.4.1 Pegasus Monitoring and Measuring Service; 6.5 Conclusion; References; Chapter 7: Workflow Predictions Through Operational Analytics and Machine Learning; 7.1 Introduction; 7.2 Workflow Prediction; 7.2.1 Challenges in Designing an APPS; 7.2.2 Workflow Prediction Approaches; 7.3 AM-Based Performance Prediction Systems.
Bibliography Note Includes bibliographical references and index.
Summary 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.
Note Print version record.
ISBN 9783319569826 (electronic bk.)
3319569821 (electronic bk.)
ISBN/ISSN 10.1007/978-3-319-56982-6
OCLC # 988278540
Additional Format Print version: 9783319569819 3319569813 (OCoLC)978290107

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