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
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Abstract	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.
AbstractList	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.
Author	Badal, Tapas Kumar, Naween Jaiprakash, Sahani Pooja
Author_xml	– sequence: 1 givenname: Sahani Pooja orcidid: 0000-0003-2857-9555 surname: Jaiprakash fullname: Jaiprakash, Sahani Pooja email: sahanipooja26@gmail.com organization: School of Computer Science Engineering and Technology (SCSET), Bennett University, Department of CSE, ITER, Siksha ’O’ Anusandhan (Deemed to be University) – sequence: 2 givenname: Tapas surname: Badal fullname: Badal, Tapas organization: School of Computer Science Engineering and Technology (SCSET), Bennett University – sequence: 3 givenname: Naween orcidid: 0000-0001-7062-0131 surname: Kumar fullname: Kumar, Naween organization: School of Computer Science Engineering and Technology (SCSET), Bennett University
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Title	EEWS: energy-efficient multi-objective workflow scheduling in IaaS cloud environments with CP-FPA optimization
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