Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing
This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within h...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2020-04-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125938908/view |
| _version_ | 1828395903271043072 |
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| author | Boonhatai Kruekaew Warangkhana Kimpan |
| author_facet | Boonhatai Kruekaew Warangkhana Kimpan |
| author_sort | Boonhatai Kruekaew |
| collection | DOAJ |
| description | This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO. |
| first_indexed | 2024-12-10T08:20:11Z |
| format | Article |
| id | doaj.art-813a74e9204843d7a29401d7e06925f8 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T08:20:11Z |
| publishDate | 2020-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-813a74e9204843d7a29401d7e06925f82022-12-22T01:56:21ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-04-0113110.2991/ijcis.d.200410.002Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud ComputingBoonhatai KruekaewWarangkhana KimpanThis paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.https://www.atlantis-press.com/article/125938908/viewArtificial bee colony algorithmCloud computingScheduling algorithmsLoad balanceResource managementDistribution |
| spellingShingle | Boonhatai Kruekaew Warangkhana Kimpan Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing International Journal of Computational Intelligence Systems Artificial bee colony algorithm Cloud computing Scheduling algorithms Load balance Resource management Distribution |
| title | Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing |
| title_full | Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing |
| title_fullStr | Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing |
| title_full_unstemmed | Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing |
| title_short | Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing |
| title_sort | enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing |
| topic | Artificial bee colony algorithm Cloud computing Scheduling algorithms Load balance Resource management Distribution |
| url | https://www.atlantis-press.com/article/125938908/view |
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