Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach: From Theory to Practice: Designing and Assessing Energy-Efficient Load Balancing Algorithms for Cloud Computing - Couverture souple

Junaid, Muhammad

Couverture souple

ISBN 10 : 620748729X ISBN 13 : 9786207487295

Editeur : LAP LAMBERT Academic Publishing, 2024

Synopsis

Maintaining accuracy in load balancing using metaheuristics poses challenges despite recent hybrid approaches. Optimized metaheuristic methods are employed to balance loads in the cloud efficiently. Multi-objective Quality of Service (QoS) metrics like reduced SLA violations, makespan, high throughput, and low energy consumption are crucial. Cloud applications, being computation-intensive, demand effective load balancing to prevent poor solutions due to exponential memory growth.To enhance load balancing in cloud computing, a new hybrid model is proposed, performing file classification using Filetype formatting. Three algorithms—Ant Colony Optimization using Filetype Formatting (ACOFTF), Data Format Classification using Support Vector Machine (DFC-SVM), and Datatype Formatting DFTF/DTF—are developed.Overall, the proposed hybrid metaheuristic approaches offer promising solutions for enhancing load balancing in cloud computing environments.

Détails de l'édition

• Éditeur: LAP LAMBERT Academic Publishing
• Date d'édition: 2024
• Langue: anglais
• ISBN 10: 620748729X
• ISBN 13: 9786207487295
• Reliure: Broché
• Nombre de pages: 356
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible