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Machine Learning and Optimization Models for Optimization in Cloud.

Contributor(s): Material type: TextTextSeries: Chapman & Hall/Distributed Computing and Intelligent Data Analytics SeriesPublisher: [Place of publication not identified] : Chapman and Hall/CRC, 2022Edition: First editionDescription: 1 online resource (232 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781003185376
  • 1003185371
  • 9781000542257
  • 1000542254
  • 9781000542264
  • 1000542262
Subject(s): DDC classification:
  • 006.31 23
LOC classification:
  • Q325.5
Online resources:
Contents:
1. Introduction to Virtualization in Cloud ComputingVijay Kumar Sharma, Arjun Singh, Jaya Krishna R, Amit kumar bairwa2.Machine Learning, Deep Learning Based Optimization in Multilayered CloudPunit Gupta, Mayank Kumar Goyal 3. Neural Network Based Resource Allocation Model in Multilayered CloudRohit Verma, Punit Gupta4.Consideration For Availability And Reliability In Cloud ComputingDheeraj Rane, Vaishali Chourey, Rohit Verma, Punit Gupta5.Neural Network and Deep Learning Based Resource Allocation Model For Multilayered CloudSanjeet Bhagat, Punit Gupta6.Machine Learning Based Predictive Model To Improve Cloud Application Performance In Cloud SAAS.Falguni Sharma, Punit Gupta7.Fault Aware Machine Learning and Deep Learning Based Algorithm For Cloud Architecture.Deepika Agarwal, Sneha Agrawal, Punit Gupta8.Energy Efficient VM Placement Using Back Propagation Neural Network and Genetic Algorithm Oshin Sharma, Hemraj Saini, Geetanjali Rathee9. Meta-Heuristic Algorithm for Power Efficiency in CloudShally, Sanjay Kumar Sharma, Sunil Kumar10.Intelligent Scalable Algorithm for Resource Efficiency in CloudDr. Arjun Singh, Dr. Punit Gupta, Dr. Vijay Kumar Sharma, Tarun Jain, Surbhi Chauhan
Summary: 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.
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1. Introduction to Virtualization in Cloud ComputingVijay Kumar Sharma, Arjun Singh, Jaya Krishna R, Amit kumar bairwa2.Machine Learning, Deep Learning Based Optimization in Multilayered CloudPunit Gupta, Mayank Kumar Goyal 3. Neural Network Based Resource Allocation Model in Multilayered CloudRohit Verma, Punit Gupta4.Consideration For Availability And Reliability In Cloud ComputingDheeraj Rane, Vaishali Chourey, Rohit Verma, Punit Gupta5.Neural Network and Deep Learning Based Resource Allocation Model For Multilayered CloudSanjeet Bhagat, Punit Gupta6.Machine Learning Based Predictive Model To Improve Cloud Application Performance In Cloud SAAS.Falguni Sharma, Punit Gupta7.Fault Aware Machine Learning and Deep Learning Based Algorithm For Cloud Architecture.Deepika Agarwal, Sneha Agrawal, Punit Gupta8.Energy Efficient VM Placement Using Back Propagation Neural Network and Genetic Algorithm Oshin Sharma, Hemraj Saini, Geetanjali Rathee9. Meta-Heuristic Algorithm for Power Efficiency in CloudShally, Sanjay Kumar Sharma, Sunil Kumar10.Intelligent Scalable Algorithm for Resource Efficiency in CloudDr. Arjun Singh, Dr. Punit Gupta, Dr. Vijay Kumar Sharma, Tarun Jain, Surbhi Chauhan

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.

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