## 4 Proposed Algorithm
### Normalize ECT
We observed that, when the makespan was low, the success rate was consequently higher on the MONWS in comparison to HEFT, DLS, min-min, max-min, and NMMWS. As a result,

Figure 12: Speedup. Figure 13: Load balance. MONWS chooses the VMs with the earliest possible finish time (EFT) for optimally executing the workflow. Figure 16: Success rate on the Epigenimics workflow. Figure 14: Success rate on the Montage workflow. Figure 15: Success rate on the Cybershake workflow. ## 6 Conclusions

This study proposed a novel MONWS algorithm by taking into consideration the dynamic priority of tasks based on min-max in order to minimize makespan and cost and maximize cloud utilization for optimum workflow scheduling. In the proposed algorithm in this study, the tasks were initially moved to the ready list, followed by the calculation of the estimated computation time and early finish time. Finally, the dynamic threshold values were compared in order to divide the tasks into groups (b_large and b_small) for scheduling the tasks on virtual machines. For implementation, the cloudsim simulation tool was utilized, and the results exhibited that the proposed algorithm outperformed the existing algorithms such as HEFT, DLS, min-min, max-min, and NMMWS with respect to real-world scientific workflow applications in terms of makespan, cost, cloud utilization, and load balancing. The MONWS proposed in this study achieved a 35% improvement in makespan, an 8% increase in maximum average cloud utilization, and a 4% decrease in cost. However, the proposed algorithm needs improvement to handle cases of VM failure and checking energy consumption. The proposed algorithm also did not consider handling multiple workflows dynamically. These could potentially form the basis of future research. Conceptualization, V.R.P. ; Methodology, G.R.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data of written code, all results of photo will be made available on request. The authors declare no conflict of interest. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] AdPSO: Adaptive PSO-Based task scheduling approach for cloud computing. _Sensors_**2022**, _22_, 920. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deadline-Constrained cost optimization approaches for workflow scheduling in clouds. _IEEE Trans. Parallel Distrib. Syst._**2017**, _28_, 3401-3412. [CrossRef]
* _[PERSON] et al. (2008)_ [PERSON]; [PERSON]; [PERSON] _Workflow Scheduling Algorithms for Grid Computing_; Springer: Berlin/Heidelberg, Germany, 2008; Volume 146. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] MCTE: Minimizes task completion time and execution cost to optimize scheduling performance for smart grid cloud. _IEEE Access_**2019**, \(7\), 134793-134803. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] An analysis of a meta heuristic optimization algorithms for cloud computing. In Proceedings of the 2021 5 th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 22-23 October 2021. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] A hybrid metaheuristic for multi-objective scientific workflow scheduling in a cloud environment. _Appl. Sci._**2018**, \(8\), 538. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Energy-efficient load balancing algorithm for workflow scheduling in cloud data centers using queuing and thresholds. _Appl. Sci._**2021**, _11_, 5849. [CrossRef]
* _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] DAGMap: Efficient and dependable scheduling of DAG workflow job in grid. _J. Supercomput._**2010**, _51_, 201-223. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] MOELS: Multiobjective evolutionary list scheduling for cloud workflows. _IEEE Trans. Autom. Sci. Eng._**2020**, _17_, 166-176.