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

Muhammad Junaid

ISBN 10: 620748729X / ISBN 13: 9786207487295

Editore: LAP LAMBERT Academic Publishing, 2024

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9786207487295

Dati bibliografici

• Titolo: Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach : From Theory to Practice: Designing and Assessing Energy-Efficient Load Balancing Algorithms for Cloud Computing
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 620748729X
• ISBN 13: 9786207487295
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

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.