Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes
Abstract Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-01-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00374-7 |
| _version_ | 1811175715281829888 |
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| author | Tao Hai Jincheng Zhou Dayang Jawawi Dan Wang Uzoma Oduah Cresantus Biamba Sanjiv Kumar Jain |
| author_facet | Tao Hai Jincheng Zhou Dayang Jawawi Dan Wang Uzoma Oduah Cresantus Biamba Sanjiv Kumar Jain |
| author_sort | Tao Hai |
| collection | DOAJ |
| description | Abstract Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems. |
| first_indexed | 2024-04-10T19:41:24Z |
| format | Article |
| id | doaj.art-d1860018773043eebdbb3f5b1cac1b99 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-10T19:41:24Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-d1860018773043eebdbb3f5b1cac1b992023-01-29T12:21:57ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-01-0112111210.1186/s13677-022-00374-7Task scheduling in cloud environment: optimization, security prioritization and processor selection schemesTao Hai0Jincheng Zhou1Dayang Jawawi2Dan Wang3Uzoma Oduah4Cresantus Biamba5Sanjiv Kumar Jain6School of Computer and Information, Qiannan Normal University for NationalitiesSchool of Computer and Information, Qiannan Normal University for NationalitiesFaculty of Computing, Universiti Teknologi Malaysia (UTM)Key Laboratory of Complex Systems and Intelligent Optimization of GuizhouDepartment of Physics, Faculty of Science, University of LagosSchool of Mathematics and Statistics, Department of Educational Sciences, Faculty of Education and Business Studies, University of GävleElectrical Engineering Department, Medi-Caps UniversityAbstract Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems. https://doi.org/10.1186/s13677-022-00374-7HEFT AlgorithmCloud ComputingTask SchedulingNP-complete |
| spellingShingle | Tao Hai Jincheng Zhou Dayang Jawawi Dan Wang Uzoma Oduah Cresantus Biamba Sanjiv Kumar Jain Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes Journal of Cloud Computing: Advances, Systems and Applications HEFT Algorithm Cloud Computing Task Scheduling NP-complete |
| title | Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes |
| title_full | Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes |
| title_fullStr | Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes |
| title_full_unstemmed | Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes |
| title_short | Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes |
| title_sort | task scheduling in cloud environment optimization security prioritization and processor selection schemes |
| topic | HEFT Algorithm Cloud Computing Task Scheduling NP-complete |
| url | https://doi.org/10.1186/s13677-022-00374-7 |
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