EEWS: energy-efficient multi-objective workflow scheduling in IaaS cloud environments with CP-FPA optimization

With the rapid growth of cloud data centers, concerns about energy consumption and its impact on natural resources have become increasingly critical. In response, organizations and global communities are actively pursuing strategies to enhance energy efficiency, particularly in areas such as server...

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Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 17; H. 5; S. 3157 - 3172
Hauptverfasser: Jaiprakash, Sahani Pooja, Badal, Tapas, Kumar, Naween
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Cloud computing
Computer centers
Computer Imaging
Computer Science
Cooling systems
Costs
Critical path
Energy consumption
Genetic algorithms
Heuristic
Heuristic methods
Image Processing and Computer Vision
Machine Learning
Methods
Multiple objective analysis
Natural resources
Optimization
Original Research
Pattern Recognition and Graphics
Plant reproduction
Scheduling
Software Engineering
Virtual environments
Vision
Workflow
Workloads
Workflow
Critical path
Makespan
Flower-pollination
Optimizing scheduling
Energy efficient
ISSN:2511-2104, 2511-2112
Online-Zugang:Volltext
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Zusammenfassung:With the rapid growth of cloud data centers, concerns about energy consumption and its impact on natural resources have become increasingly critical. In response, organizations and global communities are actively pursuing strategies to enhance energy efficiency, particularly in areas such as server operations, cooling systems, and heating technologies. This paper introduces a novel approach to workflow scheduling in cloud environments, leveraging the MaxUtil model. At its core is the Critical Path-based Flower Pollination Algorithm (CP-FPA), a robust metaheuristic designed to minimize energy consumption while simultaneously optimizing makespan. The proposed methodology operates in two key phases: (i) allocating tasks to active virtual machines and (ii) scheduling these tasks using optimal or near-optimal heuristics. The model incorporates a dynamic multi-objective fitness function and an efficient pollen representation mechanism. Extensive computational experiments were conducted across five scientific applications, demonstrating the algorithm’s rapid convergence and the effectiveness of the proposed solution. Comparative analysis against established scheduling algorithms such as PSO, GSA, GA, BAT, and ACO reveals that the CP-FPA approach consistently outperforms these methods in various scenarios.
Bibliographie:ObjectType-Article-1
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-025-02490-4