Narasimhan, V. Lakshmi and Jithin, V. S. and Ananya, M. and Jonathan, Oluranti (2022) AI-Based Enhanced Time Cost-Effective Cloud Workflow Scheduling. In: Artificial Intelligence for Cloud and Edge Computing. Springer Nature.
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Abstract
In this paper, two scheduling algorithms are presented, namely, timeconstrained early-deadline cost-effective algorithm (TECA) to schedule these time-sensitive workflows at the lowest cost and versatile time-cost algorithm (VTCA) which consider both time and cost constraints; these algorithms considerably enhance the earlier algorithms. TECA schedules activities to be completed as soon as possible and optimizes the costs in resource provisioning. VTCA supports quality of service (QoS)-based scheduling, keeping a balance between completion time and cost for the selected QoS level. Both algorithms schedule tasks of the same height within the minimum completion time (using Max-Min algorithm). The tasks get scheduled on new resources only when their completion times are more than the calculated minimum completion times for the given resource. CloudSim-based results show that our algorithms minimize completion time better than other popular algorithms, in addition to reducing costs. The modeling for costs satisfies the criteria of earliest completion time.
| Item Type: | Book Section |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | nwokealisi |
| Date Deposited: | 12 Jul 2024 16:06 |
| Last Modified: | 12 Jul 2024 16:06 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18197 |
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