## 3 Materials and Methods
### Proposed Algorithm
The tasks are classified accordingly, and resources are created. The proposed algorithm was validated on benchmark datasets, and comparative experimental results were presented in terms of the makespan, energy efficiency, and load balancing. The results were compared with those of standard algorithms and specialized

Figure 5: Percent improvement gain of the proposed algorithm over the other methods on the Cybershake dataset. Figure 6: Percent improvement gain of the proposed algorithm over the other methods on the Epigenomics dataset. schedulers. The results showed that the proposed algorithm achieved better results than those of the other methods in terms of all parameters. A percent improvement gain from 13 to 53 percent was achieved. The issues of VM migration and adaptive thresholds require further investigation in order to further improve the solution and achieve better results. The scheduling problem consists of many parameters, but some parameters are contradictory. The designed solutions must consider effects on other parameters while improving the desired parameters. Security and privacy are examples of such parameters that need to be addressed, in addition to other parameters. The implementation of the simulated algorithm in actual environments will also bring challenges, such as the administration cost, energy consumed by factors other than computing, failures in hardware, and data backups. Conceptualization, N.M. and M.S. ; methodology, N.M.; software, N.M.; validation, M.T., G.A. and M.S. ; formal analysis, M.S. and G.A. ; investigation, N.M.; resources, M.S. and M.T. ; data curation, N.M., M.T., and G.A. ; writing--original draft preparation, N.M.; writing--review and editing, M.S., G.A., B.S. and F.M. ; visualization, M.T. ; supervision, M.S. ; project administration, F.M. and B.S. ; funding acquisition, F.M. and B.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the FCT--Fundacao para a Ciencia e a Tecnologia, I.P. [Project UIDB/05105/2020] and Cluster Research Grant R20143 of Zayed University, UAE. The datasets used are publicly available. The authors declare conflicts of Interest: There is no conflict of interest to report. ## References

* _J.J._J. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-objective approach of energy efficient workflow scheduling in cloud environments. _Concurr. Comput. Pract. Exp._**2019**, _31_, e4949. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] [PERSON] A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling. _Parallel Comput._**2017**, _62_, 1-19. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing. _Int. J. Distrib. Sens. Netw._**2020**, _16_, 1550147720949142. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers. _Future Gener. Comput. Syst._**2020**, _106_, 221-233. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] [PERSON] A hybrid energy-Aware virtual machine placement algorithm for cloud environments. _Expert Syst. Appl._**2020**, _150_, 113306. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON] [PERSON]; [PERSON] [PERSON] Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. _J. Ambient. Intell. Humaniz. Comput._**2020**, _12_, 4147-4159. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A multi-objective optimization scheme for job scheduling in sustainable cloud data centers. _IEEE Trans. Cloud Comput._**2019**. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A study on energy consumption of DVFS and Simple VM consolidation policies in cloud computing data centers using CloudSim Toolkit. _Wirel. Pers.