Autonomic computing in cloud resource management in Industry 4.0 / Tanupriya Choudhury, Bhupesh Kumar Dewangan, Ravi Tomar, Bhupesh Kumar Singh, Teoh Teik Toe, Nguyen Gia Nhu, editors.
Choudhury, Tanupriya, editor.; Dewangan, Bhupesh Kumar, editor.; Tomar, Ravi, editor.; Singh, Bhupesh Kumar, editor.; Toe, Teoh Teik, editor.; Nhu, Nguyen Gia, editor.
2021
QA76.9.A97 A88 2021
Available Online

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Title Autonomic computing in cloud resource management in Industry 4.0 / Tanupriya Choudhury, Bhupesh Kumar Dewangan, Ravi Tomar, Bhupesh Kumar Singh, Teoh Teik Toe, Nguyen Gia Nhu, editors.
ISBN 9783030717568 (electronic bk.)
3030717569 (electronic bk.)
9783030717551
3030717550
DOI https://doi.org/10.1007/978-3-030-71756-8
Published Cham : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource (xvi, 409 pages) : illustrations (some colour)
Item Number 10.1007/978-3-030-71756-8 doi
Call Number QA76.9.A97 A88 2021
Dewey Decimal Classification 004
Summary This book describes the next generation of industry "Industry 4.0 "and how it holds the promise of increased flexibility in manufacturing, along with automation, better quality, and improved productivity. The authors discuss how it thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. The authors posit that intelligent cloud services and resource sharing play an important role in Industry 4.0 anticipated Fourth Industrial Revolution. This book serves the different issues and challenges in cloud resource management CRM techniques with proper propped solution for IT organizations. The book features chapters based on the characteristics of autonomic computing with its applicability in CRM. Each chapter features the techniques and analysis of each mechanism to make better resource management in cloud. Describes self-adaptable framework to find the best optimization techniques automatically based on issues and platforms; Presents a self-optimized model to find the optimal resource from the resource pool; Includes applications to diagnose and heal the system failure, and protect from malicious attacks
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 23, 2021).
Added Author Choudhury, Tanupriya, editor.
Dewangan, Bhupesh Kumar, editor.
Tomar, Ravi, editor.
Singh, Bhupesh Kumar, editor.
Toe, Teoh Teik, editor.
Nhu, Nguyen Gia, editor.
Series EAI/Springer innovations in communication and computing. 2522-8595
Available in Other Form Print version: 9783030717551
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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Emerging paradigms and practices in cloud resource management
Autonomic computing in Cloud : model and applications
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Issues and challenges in autonomic computing and resource management
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Resource management issues and challenges in autonomic computing
Classification of various scheduling approaches for resource management system in cloud computing
Optimization in autonomic computing and resource management
A proposed framework for autonomic resource management in Cloud computing environment
Autonomic computing on cloud computing using architecture adoption models : an empirical review
Self-protection approach for cloud computing
Elastic security for autonomic computing using intelligent algorithm
The architecture of autonomic cloud resource management
Towards Industry 4.0 through cloud resource management
A walkthrough in live migration strategies for energy-aware resources management in cloud
Virtual machine scaling in autonomic cloud resource management
Autonomic resource management in a cloud-based infrastructure environment
Digital dimensions of Industry 4.0 : opportunities for autonomic computing and applications
A study of resource management and security-based techniques in autonomic cloud computing.

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