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Machine Learning and Optimization Models for Optimization in Cloud (Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series) - Tapa dura
Sinopsis
Machine Learning and Models for Optimization in Cloud's 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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Punit Gupta is Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India, from 2018. He received B.Tech. Degree in Computer Science and Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya ,Madhya Pradesh in 2010. He received M.Tech. Degree in Computer Science and Engineering from Jaypee Institute of Information Technology (Deemed university) in 2012 On "Trust Management in Cloud computing" .He is a Gold Medalist in M-Tech. He has been awarded doctoral degree in Feb 2017. He has research experience in Internet-of-Things, Cloud Computing, and Distributed algorithms and authored more than 70 research papers in referred reputed journals and international conferences. He is currently serving as a Member of Computer Society of India (CSI), Member of IEEE, Professional member of ACM. HE has authored 15 books of springer, IGI and many more.
Mayank K Goyal is an Assistant professor at Sharda University, India. He received M.Tech. Degree in Computer Science and Engineering from Jaypee Institute of Information Technology (Deemed university) in. He has been awarded doctoral degree in Feb 2019. He has research experience in Internet-of-Things, Cloud Computing, and Distributed algorithms and authored more than 50 research papers in referred reputed journals and international conferences. He is currently serving as a Member of Computer Society of India (CSI), Member of IEEE, Professional member of ACM.
Sudeshna Chakraborty is an consolidation of 15 years industry academics experiences . Research Group Head and Associate Professor of Computer Science & Engineering Department at Sharda University,Greater Noida. She is PhD in Computer Science & Engineering with Neural Network & Semantic Web Engineering. She has acquired several awards as best teacher , research excellence award by Inst of Scholars, keynote speaker, for best Paper Presenter (IEI) , Organizing member of International conference , Reviewer committee, Session Chairs, Institute of Engineers ,InSc and ATAL AICTE sponsored FDP and other FDP as a speaker. she has filed 8 patent in the field of Robotic , solar energy & sensors , chaired IEEE conference in Paris ICACCE 2018 & keynote Speaker Springer conference in Tunisia ICS2A, Track Chair Smart Tecnologies and Artificial Intelligence spain. She is active member of professional society like IEEE (USA), IEI, IETA and Academic.
Ahmed A Elngar is an assistant professor at Faculty of Computers & Artificial Intelligence, Beni-Suef University, Egypt. He is Director of Technological and Informatics Studies Center at Beni-Suef University. He is managing editor of Journal of Cyber Security and Information Management (JCIM). The professor completed his Doctor of Philosophy (Ph.D) of Computer Science, Faculty of Science from Al-Azhar University – Cairo, Egypt in 2016. He has over 30 research contributions on reputed journals and conferences. He also have 11 books published with reputed publishers.
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Machine learning and Optimization Models for Optimization in Cloud
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Punit Gupta is Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India, from 2018. He received B.Tech. Degree in Computer Science and Engineering from Rajiv Gan. Nº de ref. del artículo: 510111955
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Machine Learning and Optimization Models for Optimization in Cloud
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Taylor & Francis Ltd (Sales) Feb 2022, 2022
ISBN 10: 1032028203
ISBN 13: 9781032028200
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning and Models for Optimization in Cloud's 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. 204 pp. Englisch. Nº de ref. del artículo: 9781032028200
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Machine Learning and Optimization Models for Optimization in Cloud
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Chapman and Hall/CRC, 2022
ISBN 10: 1032028203
ISBN 13: 9781032028200
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Machine Learning and Optimization Models for Optimization in Cloud (Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series)
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Machine Learning and Optimization Models for Optimization in Cloud
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ISBN 13: 9781032028200
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Machine Learning and Optimization Models for Optimization in Cloud (Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series)
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ISBN 10: 1032028203
ISBN 13: 9781032028200
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Machine Learning and Optimization Models for Optimization in Cloud
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ISBN 13: 9781032028200
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning and Models for Optimization in Cloud's 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. Nº de ref. del artículo: 9781032028200
Cantidad disponible: 2 disponibles