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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 20 July 2026
- ISBN 9781032600093
- Binding Paperback
- No. of pages262 pages
- Size 234x156 mm
- Language English
- Illustrations 43 Illustrations, black & white; 43 Line drawings, black & white; 18 Tables, black & white 700
Categories
Short 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 meta-heuristic approaches for placement techniques.
MoreLong 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.
MoreTable of Contents:
1. Introduction To Next Generation Optimization In Cloud Computing Services 2: Challenges And Open Issues In Cloud Computing Services 3. Resource Management In Cloud Using Nature Inspired Algorithm 4.Machine Learning approaches for effective energy efficient resource management strategies in cloud services 5. Efficient Virtual Machine Allocation Technique Based On Hybrid Approach 6. Optimizing resource allocation in the cloud using deep learning 7. Reliable Resource Optimization Model for Cloud using Adversarial Neural Network 8. Efficient Migration Technique for Load Balancing in Cloud 9. Cost optimization model for cloud using Machine learning and Artificial intelligencd 10. Scalable optimization algorithm for Cloud resource scaling 11. Fault aware optimization using Machine Learning and Artificial Intelligence 12. Tools and Open Source Platforms for Cloud Computing
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