Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach

Muhammad Junaid

ISBN 10: 620748729X / ISBN 13: 9786207487295

Editore: LAP LAMBERT Academic Publishing Apr 2024, 2024

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Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 356 pp. Englisch.

Codice articolo 9786207487295

Dati bibliografici

• Titolo: Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach
• Editore: LAP LAMBERT Academic Publishing Apr 2024
• 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.