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

Published by LAP LAMBERT Academic Publishing, 2024

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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 | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207487295 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 129155974

Bibliographic Details

• Title: Harnessing Data Types for Energy Efficiency: Innovative Cloud Approach | From Theory to Practice: Designing and Assessing Energy-Efficient Load Balancing Algorithms for Cloud Computing
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 620748729X
• ISBN 13: 9786207487295
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 22D millimeters 220 millimeters width by 150 millimeters height by 22 millimeters depth
• Weight: 548 grams
• Seller catalogs:
  • Bücher

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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.