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ISBN 10: 1032028246 ISBN 13: 9781032028248
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Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
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ISBN 10: 1032028246 ISBN 13: 9781032028248
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Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
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Published by Chapman and Hall/CRC, 2025
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ISBN 10: 1032028246 ISBN 13: 9781032028248
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Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
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ISBN 10: 1032028246 ISBN 13: 9781032028248
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Paperback. Condition: new. Paperback. Machine Learning and Models for Optimization in Clouds main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features Comprehensive introduction to cloud architecture and its service models. Vulnerability and issues in cloud SAAS, PAAS and IAAS Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models Detailed study of optimization techniques, and fault management techniques in multi layered cloud. Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. Advanced study of algorithms using artificial intelligence for optimization in cloud Method for power efficient virtual machine placement using neural network in cloud Method for task scheduling using metaheuristic algorithms. A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence. Cloud computing has been a new trend in problem-solving and providing reliable computing platform for big and high computational tasks. This technique is used for business industries like banking, trading and many e-commerce businesses to accommodate high request rate, high availability for all time without stopping system and system failure. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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ISBN 10: 1032028246 ISBN 13: 9781032028248
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Paperback. Condition: new. Paperback. Machine Learning and Models for Optimization in Clouds main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features Comprehensive introduction to cloud architecture and its service models. Vulnerability and issues in cloud SAAS, PAAS and IAAS Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models Detailed study of optimization techniques, and fault management techniques in multi layered cloud. Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. Advanced study of algorithms using artificial intelligence for optimization in cloud Method for power efficient virtual machine placement using neural network in cloud Method for task scheduling using metaheuristic algorithms. A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence. Cloud computing has been a new trend in problem-solving and providing reliable computing platform for big and high computational tasks. This technique is used for business industries like banking, trading and many e-commerce businesses to accommodate high request rate, high availability for all time without stopping system and system failure. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Chapman and Hall/CRC, 2022
ISBN 10: 1032028203 ISBN 13: 9781032028200
Language: English
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ISBN 10: 1032028203 ISBN 13: 9781032028200
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ISBN 10: 1032028203 ISBN 13: 9781032028200
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ISBN 10: 1032028203 ISBN 13: 9781032028200
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ISBN 10: 6204109197 ISBN 13: 9786204109190
Language: French
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ISBN 10: 6204109219 ISBN 13: 9786204109213
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Published by Taylor & Francis Ltd, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
Language: English
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Paperback. Condition: new. Paperback. Machine Learning and Models for Optimization in Clouds main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features Comprehensive introduction to cloud architecture and its service models. Vulnerability and issues in cloud SAAS, PAAS and IAAS Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models Detailed study of optimization techniques, and fault management techniques in multi layered cloud. Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. Advanced study of algorithms using artificial intelligence for optimization in cloud Method for power efficient virtual machine placement using neural network in cloud Method for task scheduling using metaheuristic algorithms. A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence. Cloud computing has been a new trend in problem-solving and providing reliable computing platform for big and high computational tasks. This technique is used for business industries like banking, trading and many e-commerce businesses to accommodate high request rate, high availability for all time without stopping system and system failure. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Chapman And Hall/CRC, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
Language: English
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Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032028246 ISBN 13: 9781032028248
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Taschenbuch. Condition: Neu. Machine Learning and Optimization Models for Optimization in Cloud | Punit Gupta (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032028248 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.