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Anglický jazyk
Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach
Autor: Muhammad Junaid
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... Viac o knihe
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O knihe
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.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2024
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786207487295
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