Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing
Abstract Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00401-1 |
| _version_ | 1797845719826038784 |
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| author | G. Saravanan S. Neelakandan P. Ezhumalai Sudhanshu Maurya |
| author_facet | G. Saravanan S. Neelakandan P. Ezhumalai Sudhanshu Maurya |
| author_sort | G. Saravanan |
| collection | DOAJ |
| description | Abstract Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations. |
| first_indexed | 2024-04-09T17:44:37Z |
| format | Article |
| id | doaj.art-0c1789198a4e478a8d49f6714f54bf30 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-09T17:44:37Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-0c1789198a4e478a8d49f6714f54bf302023-04-16T11:25:16ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-02-0112111410.1186/s13677-023-00401-1Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computingG. Saravanan0S. Neelakandan1P. Ezhumalai2Sudhanshu Maurya3Department of Computer Science and Engineering, Erode Sengunthar Engineering CollegeDepartment of Computer Science and Engineering, R.M.K Engineering CollegeDepartment of Computer Science and Engineering, R.M.D Engineering CollegeSchool of Computing, Graphic Era Hill UniversityAbstract Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.https://doi.org/10.1186/s13677-023-00401-1Task schedulingWild Horse Optimization (WHO)Cloud computingUtilization of resourcesMetaheuristic algorithms |
| spellingShingle | G. Saravanan S. Neelakandan P. Ezhumalai Sudhanshu Maurya Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing Journal of Cloud Computing: Advances, Systems and Applications Task scheduling Wild Horse Optimization (WHO) Cloud computing Utilization of resources Metaheuristic algorithms |
| title | Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| title_full | Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| title_fullStr | Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| title_full_unstemmed | Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| title_short | Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| title_sort | improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing |
| topic | Task scheduling Wild Horse Optimization (WHO) Cloud computing Utilization of resources Metaheuristic algorithms |
| url | https://doi.org/10.1186/s13677-023-00401-1 |
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