Reliable and Intelligent Optimization in Multi-Layered Cloud Computing Architectures [Paperback]

1. Introduction to Optimization in Cloud Computing. 2. Improve QoS and Resource Efficiency in Cloud Using Neural Network. 3. Machine Learning-Based Optimization Approach to Analyze Text-Based Reviews for Improving Graduation Rates for Cloud-Based Architectures. 4. An Energy-Aware Optimization Model Using a Hybrid Approach. 5. Fault Tolerant and Reliable Resource Optimization Model for Cloud. 6. Asynchronous Checkpoint/Restart Fault Tolerant Model for Cloud. 7. Fault Prediction Models for Optimized Delivery of Cloud Services. 8. Secured Transactions in Storage System for Real-Time Blockchain Network Monitoring System. 9. Service Scaling and Cost- Prediction-Based Optimization in Cloud Computing. 10. Cost- and Network-Aware Metaheuristic Cloud Optimization. 11. The Role of SLA and Ethics in Cost Optimization for Cloud Computing.

One of the major developments in the computing field has been cloud computing, which enables users to do complicated computations that local devices are unable to handle. The computing power and flexibility that have made the cloud so popular do not come without challenges. It is particularly challenging to decide which resources to use, even when they have the same configuration but different levels of performance because of the variable structure of the available resources. Cloud data centers can host millions of virtual machines, and where to locate these machines in the cloud is a difficult problem. Additionally, fulfilling optimization needs is a complex problem.

Reliable and Intelligent Optimization in Multi-Layered Cloud Computing Architectures examines ways to meet these challenges. It discusses virtual machine placement techniques and task scheduling techniques that optimize resource utilization and minimize energy consumption of cloud data centers. Placement techniques presented can provide an optimal solution to the optimization problem usl3&