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Task Scheduling in Cloud Computing Based on The Cuckoo Search Algorithm | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 9, Volume 22, Issue 1, 2022, Pages 86-96 PDF (807.19 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.22.1.9 | ||
| Authors | ||
| sajjad shamkhi jaber* 1; Yossra Ali2; Nuha Ibrahim2 | ||
| 1Department of Computer Science, University of Technology, Iraq-Baghdad | ||
| 2Department of Computer Science, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. | ||
| Keywords | ||
| Task Scheduling; Multiobjective; Cloud computing; cost; Makespan | ||
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