Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach

One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mazen Farid [verfasserIn] Heng Siong Lim [verfasserIn] Chin Poo Lee [verfasserIn] Rohaya Latip [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	workflow scheduling multi-objective optimization multi-cloud environment multi-criteria decision-making (MCDM) pareto optimization scientific workflow

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 15(2023), 11, p 2047
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:11, p 2047

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym15112047

Katalog-ID:	DOAJ101183666

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allfields	10.3390/sym15112047 doi (DE-627)DOAJ101183666 (DE-599)DOAJd914c2d4432c4da782c9117a5cc35ca2 DE-627 ger DE-627 rakwb eng QA1-939 Mazen Farid verfasserin aut Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. workflow scheduling multi-objective optimization multi-cloud environment multi-criteria decision-making (MCDM) pareto optimization scientific workflow Mathematics Heng Siong Lim verfasserin aut Chin Poo Lee verfasserin aut Rohaya Latip verfasserin aut In Symmetry MDPI AG, 2009 15(2023), 11, p 2047 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:15 year:2023 number:11, p 2047 https://doi.org/10.3390/sym15112047 kostenfrei https://doaj.org/article/d914c2d4432c4da782c9117a5cc35ca2 kostenfrei https://www.mdpi.com/2073-8994/15/11/2047 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11, p 2047
spelling	10.3390/sym15112047 doi (DE-627)DOAJ101183666 (DE-599)DOAJd914c2d4432c4da782c9117a5cc35ca2 DE-627 ger DE-627 rakwb eng QA1-939 Mazen Farid verfasserin aut Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. workflow scheduling multi-objective optimization multi-cloud environment multi-criteria decision-making (MCDM) pareto optimization scientific workflow Mathematics Heng Siong Lim verfasserin aut Chin Poo Lee verfasserin aut Rohaya Latip verfasserin aut In Symmetry MDPI AG, 2009 15(2023), 11, p 2047 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:15 year:2023 number:11, p 2047 https://doi.org/10.3390/sym15112047 kostenfrei https://doaj.org/article/d914c2d4432c4da782c9117a5cc35ca2 kostenfrei https://www.mdpi.com/2073-8994/15/11/2047 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11, p 2047
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allfieldsSound	10.3390/sym15112047 doi (DE-627)DOAJ101183666 (DE-599)DOAJd914c2d4432c4da782c9117a5cc35ca2 DE-627 ger DE-627 rakwb eng QA1-939 Mazen Farid verfasserin aut Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. workflow scheduling multi-objective optimization multi-cloud environment multi-criteria decision-making (MCDM) pareto optimization scientific workflow Mathematics Heng Siong Lim verfasserin aut Chin Poo Lee verfasserin aut Rohaya Latip verfasserin aut In Symmetry MDPI AG, 2009 15(2023), 11, p 2047 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:15 year:2023 number:11, p 2047 https://doi.org/10.3390/sym15112047 kostenfrei https://doaj.org/article/d914c2d4432c4da782c9117a5cc35ca2 kostenfrei https://www.mdpi.com/2073-8994/15/11/2047 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11, p 2047
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abstract	One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.
abstractGer	One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.
abstract_unstemmed	One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.
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container_issue	11, p 2047
title_short	Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach
url	https://doi.org/10.3390/sym15112047 https://doaj.org/article/d914c2d4432c4da782c9117a5cc35ca2 https://www.mdpi.com/2073-8994/15/11/2047 https://doaj.org/toc/2073-8994
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author2	Heng Siong Lim Chin Poo Lee Rohaya Latip
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up_date	2024-07-03T19:07:54.879Z
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