A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing
Cloud computing has become the main source for executing scientific experiments. It is an effective technique for distributing and processing tasks on virtual machines. Scientific workflows are complex and demand efficient utilization of cloud resources. Scheduling of scientific workflows is conside...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8937528/ |
| _version_ | 1818323388263825408 |
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| author | Muhammad Sardaraz Muhammad Tahir |
| author_facet | Muhammad Sardaraz Muhammad Tahir |
| author_sort | Muhammad Sardaraz |
| collection | DOAJ |
| description | Cloud computing has become the main source for executing scientific experiments. It is an effective technique for distributing and processing tasks on virtual machines. Scientific workflows are complex and demand efficient utilization of cloud resources. Scheduling of scientific workflows is considered as NP-complete. The problem is constrained by some parameters such as Quality of Service (QoS), dependencies between tasks and users' deadlines, etc. There exists a strong literature on scheduling scientific workflows in cloud environments. Solutions include standard schedulers, evolutionary optimization techniques, etc. This article presents a hybrid algorithm for scheduling scientific workflows in cloud environments. In the first phase, the algorithm prepares tasks lists for PSO algorithm. Bottleneck tasks are processed on high priority to reduce execution time. In the next phase, tasks are scheduled with the PSO algorithm to reduce both execution time and monetary cost. The algorithm also monitors the load balance to efficiently utilize cloud resources. Benchmark scientific workflows are used to evaluate the proposed algorithm. The proposed algorithm is compared with standard PSO and specialized schedulers to validate the performance. The results show improvement in execution time, monetary cost without affecting the load balance as compared to other techniques. |
| first_indexed | 2024-12-13T11:11:54Z |
| format | Article |
| id | doaj.art-4d3b6d6013ca4038be508e947f18847c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:11:54Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d3b6d6013ca4038be508e947f18847c2022-12-21T23:48:42ZengIEEEIEEE Access2169-35362019-01-01718613718614610.1109/ACCESS.2019.29611068937528A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud ComputingMuhammad Sardaraz0https://orcid.org/0000-0002-7169-8683Muhammad Tahir1https://orcid.org/0000-0002-7750-8959Department of Computer Science, COMSATS University Islamabad at Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad at Attock Campus, Attock, PakistanCloud computing has become the main source for executing scientific experiments. It is an effective technique for distributing and processing tasks on virtual machines. Scientific workflows are complex and demand efficient utilization of cloud resources. Scheduling of scientific workflows is considered as NP-complete. The problem is constrained by some parameters such as Quality of Service (QoS), dependencies between tasks and users' deadlines, etc. There exists a strong literature on scheduling scientific workflows in cloud environments. Solutions include standard schedulers, evolutionary optimization techniques, etc. This article presents a hybrid algorithm for scheduling scientific workflows in cloud environments. In the first phase, the algorithm prepares tasks lists for PSO algorithm. Bottleneck tasks are processed on high priority to reduce execution time. In the next phase, tasks are scheduled with the PSO algorithm to reduce both execution time and monetary cost. The algorithm also monitors the load balance to efficiently utilize cloud resources. Benchmark scientific workflows are used to evaluate the proposed algorithm. The proposed algorithm is compared with standard PSO and specialized schedulers to validate the performance. The results show improvement in execution time, monetary cost without affecting the load balance as compared to other techniques.https://ieeexplore.ieee.org/document/8937528/Cloud computingPSOschedulingscientific workflows |
| spellingShingle | Muhammad Sardaraz Muhammad Tahir A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing IEEE Access Cloud computing PSO scheduling scientific workflows |
| title | A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing |
| title_full | A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing |
| title_fullStr | A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing |
| title_full_unstemmed | A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing |
| title_short | A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing |
| title_sort | hybrid algorithm for scheduling scientific workflows in cloud computing |
| topic | Cloud computing PSO scheduling scientific workflows |
| url | https://ieeexplore.ieee.org/document/8937528/ |
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