Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization
How to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-11-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-5903/7/4/465 |
| _version_ | 1819144446341545984 |
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| author | Ren Gao Juebo Wu |
| author_facet | Ren Gao Juebo Wu |
| author_sort | Ren Gao |
| collection | DOAJ |
| description | How to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform dynamically. Two strategies, forward-backward ant mechanism and max-min rules, are introduced to quickly find out the candidate nodes for load balancing. We formulate pheromone initialization and pheromone update according to physical resources under the cloud computing environment, including pheromone evaporation, incentive, and punishment rules, etc. Combined with task execution prediction, we define the moving probability of ants in two ways, that is, whether the forward ant meets the backward ant, or not, in the neighbor node, with the aim of accelerating searching processes. Simulations illustrate that the proposed strategy can not only provide dynamic load balancing for cloud computing with less searching time, but can also get high network performance under medium and heavily loaded contexts. |
| first_indexed | 2024-12-22T12:42:16Z |
| format | Article |
| id | doaj.art-e7e82ba29e424b2a989600dd017d4990 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-12-22T12:42:16Z |
| publishDate | 2015-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-e7e82ba29e424b2a989600dd017d49902022-12-21T18:25:25ZengMDPI AGFuture Internet1999-59032015-11-017446548310.3390/fi7040465fi7040465Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony OptimizationRen Gao0Juebo Wu1School of Information Engineering, Hubei University of Economics, Wuhan 430205, ChinaDepartment of Geography, National University of Singapore Arts Link, Singapore 117570, SingaporeHow to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform dynamically. Two strategies, forward-backward ant mechanism and max-min rules, are introduced to quickly find out the candidate nodes for load balancing. We formulate pheromone initialization and pheromone update according to physical resources under the cloud computing environment, including pheromone evaporation, incentive, and punishment rules, etc. Combined with task execution prediction, we define the moving probability of ants in two ways, that is, whether the forward ant meets the backward ant, or not, in the neighbor node, with the aim of accelerating searching processes. Simulations illustrate that the proposed strategy can not only provide dynamic load balancing for cloud computing with less searching time, but can also get high network performance under medium and heavily loaded contexts.http://www.mdpi.com/1999-5903/7/4/465load balancingcloud computingant colony optimizationswarm intelligence |
| spellingShingle | Ren Gao Juebo Wu Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization Future Internet load balancing cloud computing ant colony optimization swarm intelligence |
| title | Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization |
| title_full | Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization |
| title_fullStr | Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization |
| title_full_unstemmed | Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization |
| title_short | Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization |
| title_sort | dynamic load balancing strategy for cloud computing with ant colony optimization |
| topic | load balancing cloud computing ant colony optimization swarm intelligence |
| url | http://www.mdpi.com/1999-5903/7/4/465 |
| work_keys_str_mv | AT rengao dynamicloadbalancingstrategyforcloudcomputingwithantcolonyoptimization AT juebowu dynamicloadbalancingstrategyforcloudcomputingwithantcolonyoptimization |