Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows
Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision var...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9664570/ |
| _version_ | 1819364546667610112 |
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| author | Ya Zhou Xiaobo Jiao |
| author_facet | Ya Zhou Xiaobo Jiao |
| author_sort | Ya Zhou |
| collection | DOAJ |
| description | Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good trade-off between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows into cloud computing. |
| first_indexed | 2024-12-24T23:00:40Z |
| format | Article |
| id | doaj.art-aedda2f430844fde9b558c0640e59523 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T23:00:40Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aedda2f430844fde9b558c0640e595232022-12-21T16:35:09ZengIEEEIEEE Access2169-35362022-01-01102952296210.1109/ACCESS.2021.31391379664570Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud WorkflowsYa Zhou0https://orcid.org/0000-0001-8352-7163Xiaobo Jiao1College of Electrical and Mechanical Engineering, Xuchang University, Xuchang, ChinaInternet Department, State Grid Xuchang Power Supply Company, Xuchang, ChinaCloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good trade-off between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows into cloud computing.https://ieeexplore.ieee.org/document/9664570/Cloud computingworkflow schedulingmulti-objective optimizationevolutionary algorithm |
| spellingShingle | Ya Zhou Xiaobo Jiao Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows IEEE Access Cloud computing workflow scheduling multi-objective optimization evolutionary algorithm |
| title | Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows |
| title_full | Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows |
| title_fullStr | Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows |
| title_full_unstemmed | Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows |
| title_short | Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows |
| title_sort | knowledge driven multi objective evolutionary scheduling algorithm for cloud workflows |
| topic | Cloud computing workflow scheduling multi-objective optimization evolutionary algorithm |
| url | https://ieeexplore.ieee.org/document/9664570/ |
| work_keys_str_mv | AT yazhou knowledgedrivenmultiobjectiveevolutionaryschedulingalgorithmforcloudworkflows AT xiaobojiao knowledgedrivenmultiobjectiveevolutionaryschedulingalgorithmforcloudworkflows |