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Cloud Computing for Optimization: Foundations, Applications, and Challenges [electronic resource] / edited by Bhabani Shankar Prasad Mishra, Himansu Das, Satchidananda Dehuri, Alok Kumar Jagadev.

Contributor(s): Mishra, Bhabani Shankar Prasad [editor.] | Das, Himansu [editor.] | Dehuri, Satchidananda [editor.] | Jagadev, Alok Kumar [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Big Data: 39Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIV, 463 p. 144 illus., 112 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319736761.Subject(s): Computational intelligence | Artificial intelligence | Big data | Computational Intelligence | Artificial Intelligence | Big DataAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Nature Inspired Optimizations in Cloud Computing: Applications and Challenges.- Resource Allocation in Cloud Computing Using OptimizationTechniques.- Energy Aware Resource Allocation Model for IaaS Optimization.- A Game Theoretic Model for Cloud Federation -- Resource Provisioning Strategy for Scientific Workflows in Cloud Computing Environment --   Consolidation in Cloud Environment Using Optimization Techniques.
In: Springer Nature eBookSummary: This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.
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Nature Inspired Optimizations in Cloud Computing: Applications and Challenges.- Resource Allocation in Cloud Computing Using OptimizationTechniques.- Energy Aware Resource Allocation Model for IaaS Optimization.- A Game Theoretic Model for Cloud Federation -- Resource Provisioning Strategy for Scientific Workflows in Cloud Computing Environment --   Consolidation in Cloud Environment Using Optimization Techniques.

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

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