An Efficient Task Scheduling Approach in Cloud Computing Using Hybrid Fruit Fly and Ant Colony Optimization Techniques.
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| Title: | An Efficient Task Scheduling Approach in Cloud Computing Using Hybrid Fruit Fly and Ant Colony Optimization Techniques. |
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| Authors: | Appini, Narayana Rao1 narayanaraoappini@nbkrist.org, Premnadh, K.2 premnadh.k@sreenidhi.edu.in, Sreenu, Karnam3 sreenuk@adityauniversity.in, Gurram, Prasadu2 prasadu.g@sreenidhi.edu.in, Babu, Siddabathuni Suresh1 laksur.suresh@nbkrist.org |
| Source: | International Journal of Online & Biomedical Engineering. 2026, Vol. 22 Issue 7, p52-66. 15p. |
| Subjects: | Scheduling, Ant algorithms, Metaheuristic algorithms, Cloud computing, Load balancing (Computer networks), Resource management |
| Abstract: | Although cloud computing offers on-demand resources, effective job scheduling is still a major challenge to increase efficiency and resource usage. For large-scale, dynamic workloads, traditional algorithms s FCFS and Min-Min are straightforward yet ineffective. In order to generate high-quality task--VM mappings, this study suggests a hybrid nature-inspired algorithm called fruit fly optimization--ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA). Minimizing makespan, optimizing resource usage, and distributing load evenly among virtual machines (VM) are the objectives. Workload traces from PlanetLab and CloudSim 3.0.3 are used to assess the method in a variety of experimental scenarios involving up to 1000 jobs and numerous diverse VM. The suggested FOA-ACO methodology enhances overall cloud performance by decreasing the makespan by 9.57%, employing more resources by 7.14%, and enhancing the load balancing factor (LBF) by 23.06%. This was observed by comparing it with other approaches such as FCFS, Min-Min, solo ACO, and WOA. An effective solution to the scheduling problems in today's cloud environments is provided by this hybrid method. Future research may concentrate on refining the system to take into consideration various objectives, such as cost and reliability, enhancing its use of energy, and optimizing its performance with very massive cloud setups. Also, the approach might be refined to facilitate decision-making in real time and incorporate machine learning methods for greater flexibility. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195484305 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Efficient Task Scheduling Approach in Cloud Computing Using Hybrid Fruit Fly and Ant Colony Optimization Techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Appini%2C+Narayana+Rao%22">Appini, Narayana Rao</searchLink><relatesTo>1</relatesTo><i> narayanaraoappini@nbkrist.org</i><br /><searchLink fieldCode="AR" term="%22Premnadh%2C+K%2E%22">Premnadh, K.</searchLink><relatesTo>2</relatesTo><i> premnadh.k@sreenidhi.edu.in</i><br /><searchLink fieldCode="AR" term="%22Sreenu%2C+Karnam%22">Sreenu, Karnam</searchLink><relatesTo>3</relatesTo><i> sreenuk@adityauniversity.in</i><br /><searchLink fieldCode="AR" term="%22Gurram%2C+Prasadu%22">Gurram, Prasadu</searchLink><relatesTo>2</relatesTo><i> prasadu.g@sreenidhi.edu.in</i><br /><searchLink fieldCode="AR" term="%22Babu%2C+Siddabathuni+Suresh%22">Babu, Siddabathuni Suresh</searchLink><relatesTo>1</relatesTo><i> laksur.suresh@nbkrist.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Online+%26+Biomedical+Engineering%22">International Journal of Online & Biomedical Engineering</searchLink>. 2026, Vol. 22 Issue 7, p52-66. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Load+balancing+%28Computer+networks%29%22">Load balancing (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+management%22">Resource management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Although cloud computing offers on-demand resources, effective job scheduling is still a major challenge to increase efficiency and resource usage. For large-scale, dynamic workloads, traditional algorithms s FCFS and Min-Min are straightforward yet ineffective. In order to generate high-quality task--VM mappings, this study suggests a hybrid nature-inspired algorithm called fruit fly optimization--ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA). Minimizing makespan, optimizing resource usage, and distributing load evenly among virtual machines (VM) are the objectives. Workload traces from PlanetLab and CloudSim 3.0.3 are used to assess the method in a variety of experimental scenarios involving up to 1000 jobs and numerous diverse VM. The suggested FOA-ACO methodology enhances overall cloud performance by decreasing the makespan by 9.57%, employing more resources by 7.14%, and enhancing the load balancing factor (LBF) by 23.06%. This was observed by comparing it with other approaches such as FCFS, Min-Min, solo ACO, and WOA. An effective solution to the scheduling problems in today's cloud environments is provided by this hybrid method. Future research may concentrate on refining the system to take into consideration various objectives, such as cost and reliability, enhancing its use of energy, and optimizing its performance with very massive cloud setups. Also, the approach might be refined to facilitate decision-making in real time and incorporate machine learning methods for greater flexibility. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijoe.v22i07.61591 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 52 Subjects: – SubjectFull: Scheduling Type: general – SubjectFull: Ant algorithms Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Load balancing (Computer networks) Type: general – SubjectFull: Resource management Type: general Titles: – TitleFull: An Efficient Task Scheduling Approach in Cloud Computing Using Hybrid Fruit Fly and Ant Colony Optimization Techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Appini, Narayana Rao – PersonEntity: Name: NameFull: Premnadh, K. – PersonEntity: Name: NameFull: Sreenu, Karnam – PersonEntity: Name: NameFull: Gurram, Prasadu – PersonEntity: Name: NameFull: Babu, Siddabathuni Suresh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26268493 Numbering: – Type: volume Value: 22 – Type: issue Value: 7 Titles: – TitleFull: International Journal of Online & Biomedical Engineering Type: main |
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