DBSCAN inspired task scheduling algorithm for cloud infrastructure
Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cl...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345223000445 |
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| author | S.M.F D Syed Mustapha Punit Gupta |
| author_facet | S.M.F D Syed Mustapha Punit Gupta |
| author_sort | S.M.F D Syed Mustapha |
| collection | DOAJ |
| description | Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling. |
| format | Article |
| id | doaj-art-468b1098a3e042c0a4710e6db3acd280 |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-468b1098a3e042c0a4710e6db3acd2802025-01-27T04:22:32ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-0143239DBSCAN inspired task scheduling algorithm for cloud infrastructureS.M.F D Syed Mustapha0Punit Gupta1College of Technological Innovation, Zayed University, Dubai, United Arab EmiratesUniversity College Dublin, Dublin, Ireland; Corresponding author.Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.http://www.sciencedirect.com/science/article/pii/S2667345223000445Cloud computingDensity-based spatial clustering of applications with noise (DBSCAN)PSO (particle swarm optimization)Ant colony optimization (ACO)Virtual machine (VM) |
| spellingShingle | S.M.F D Syed Mustapha Punit Gupta DBSCAN inspired task scheduling algorithm for cloud infrastructure Internet of Things and Cyber-Physical Systems Cloud computing Density-based spatial clustering of applications with noise (DBSCAN) PSO (particle swarm optimization) Ant colony optimization (ACO) Virtual machine (VM) |
| title | DBSCAN inspired task scheduling algorithm for cloud infrastructure |
| title_full | DBSCAN inspired task scheduling algorithm for cloud infrastructure |
| title_fullStr | DBSCAN inspired task scheduling algorithm for cloud infrastructure |
| title_full_unstemmed | DBSCAN inspired task scheduling algorithm for cloud infrastructure |
| title_short | DBSCAN inspired task scheduling algorithm for cloud infrastructure |
| title_sort | dbscan inspired task scheduling algorithm for cloud infrastructure |
| topic | Cloud computing Density-based spatial clustering of applications with noise (DBSCAN) PSO (particle swarm optimization) Ant colony optimization (ACO) Virtual machine (VM) |
| url | http://www.sciencedirect.com/science/article/pii/S2667345223000445 |
| work_keys_str_mv | AT smfdsyedmustapha dbscaninspiredtaskschedulingalgorithmforcloudinfrastructure AT punitgupta dbscaninspiredtaskschedulingalgorithmforcloudinfrastructure |