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Advanced Computing Techniques for Optimization in Cloud

Bibliographic Details
Title: Advanced Computing Techniques for Optimization in Cloud
Description: This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud data centers Presents the role of machine learning and metaheuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data center resource management, load balancing and orchestration using machine learning techniques Analyses dynamic and scalable resource scheduling with a focus on resource management The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.
Authors: H S Madhusudhan, Punit Gupta, Pradeep Singh Rawat
Resource Type: eBook.
Subjects: Data centers--Management, Cloud computing--Management, Machine learning--Management, Virtual computer systems--Management
Categories: COMPUTERS / Distributed Systems / Cloud Computing, COMPUTERS / Distributed Systems / General, COMPUTERS / Distributed Systems / Client-Server Computing
Database: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 3978978
RelevancyScore: 975
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 974.776672363281
IllustrationInfo
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  Data: Advanced Computing Techniques for Optimization in Cloud
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud data centers Presents the role of machine learning and metaheuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data center resource management, load balancing and orchestration using machine learning techniques Analyses dynamic and scalable resource scheduling with a focus on resource management The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3978978
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 004.6782
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Data centers--Management
        Type: general
      – SubjectFull: Cloud computing--Management
        Type: general
      – SubjectFull: Machine learning--Management
        Type: general
      – SubjectFull: Virtual computer systems--Management
        Type: general
    Titles:
      – TitleFull: Advanced Computing Techniques for Optimization in Cloud
        Type: main
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
            – D: 09
              M: 10
              Type: profile
              Y: 2024
          Identifiers:
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              Value: 9781032600079
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              Value: 9781003457152
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