# A Discrete Prey-Predator Algorithm for Cloud Task Scheduling
## 3 The Proposed Discrete Prey-Predator Algorithm for Cloud Task Scheduling
### Pseudo-Code for the Proposed Discrete Prey-Predator Algorithm for Cloud Task Scheduling
The average execution times further confirmed the algorithm's superiority, with an average execution time of 270 for the Discrete Prey-Predator algorithm compared with 315 for the firefly algorithm. These findings illustrate the algorithm's scalability and effectiveness in optimizing execution time even in more demanding cloud environments. ## 5 Conclusions

This paper proposes a new task-scheduling mechanism based on the Discrete Prey-Predator algorithm to minimize the execution time of the task-scheduling process in cloud computing. The study started by mapping cloud task scheduling with the Discrete Prey-Predator algorithm. The proposed mechanism generates an initial random population of solutions of schedules. Each solution has a fitness value representing the Discrete Prey-Predator algorithm's survival value. The survival value corresponds to the execution times of the population schedules. The fitness function aims to minimize the execution times of the schedules. To evaluate the proposed Discrete Prey-Predator, the study conducted a simulation using the CloudSim tool. Several scenarios were implemented in the evaluation process. The simulation results found that the proposed Discrete Prey-Predator cloud-task-scheduling mechanism decreases the execution times gradually when increasing the number of iterations. The execution time reduction process continues for several iterations; then, the execution time is saturated and remains constant. Furthermore, the study conducted a simulation to compare the proposed Discrete Prey-Predator mechanism with the firefly algorithm. The simulation results revealed that the Discrete Prey-Predator mechanism outperformed the firefly algorithm for cloud job scheduling. Conceptualization, D.A.A. ; Methodology, D.A.A. and A.Y. ; Original draft preparation, D.A.A. ; Review and editing, A.Y. and A.A.; Visualization, D.A.A. and A.Y. Supervision, A.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Deputy for Research and Innovation, Ministry of Education, Kingdom of Saudi Arabia, through a grant (NU/IFC/2/SERC/-/18) under the institutional funding committee at Najran University, Kingdom of Saudi Arabia. Not applicable. Authors would like to acknowledge the support of Deputy for Research and Innovation, Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/2/SERC/-/18) under the institutional funding committee at Najran University, Kingdom of Saudi Arabia. The authors declare no conflict of interest. ## References

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