## 3 Models and Methods
### Particle Swarm Optimization
In the case of multivariate input, the percentage improvement for CPU in terms of MAE over FLPSONN, FLGANN, and FLNN is 23.25, 19.51, and 29.78, respectively. Improvement gain in memory over FLPSONN, FLGANN, and FLNN for multivariate input is 25.71, 21.21, and 33.33, respectively. Figure 6: Multivariate CPU utilization prediction for different models. Figure 7: Multiivariate memory utilization prediction for different models. ## 5 Conclusions

Predicting cloud resource utilization is an important issue to handle uncertainty in cloud computing environments. In cloud computing, resources are allocated to user applications accessible from anywhere over the Internet. To handle large number of users, the resources need to be scaled dynamically for efficient utilization, reduced energy consumption, and cost with better Quality of Service (QoS). The focus of this research work is to explore the efficiency of neural networks to predict multi-resource utilization. The proposed model uses hybrid GA-PSO to train network weights and uses FLNN for prediction. The hybrid model is capable of training a network for accurate prediction of multivariate resource utilization. The proposed model is validated with comparative experimental results. The results show that the proposed hybrid model yields better accuracy as compared to traditional techniques. It can also be concluded that due to the possibility of rapid and excessive changes in resource utilization, the prediction of multi-variate resource utilization is a challenging task. The potential directions for future research can be to evaluate neural network predictors further in other areas of cloud computing, such as predicting other resources such as disk utilization, cost-effectiveness, network, and reduction in energy consumption for

Figure 8: Percentage improvement gain of the proposed model over the other models on univariate input case. Figure 9: Percentage improvement gain of the proposed models over the other models on multivariate input case. green computing. The proposed framework is evaluated with the Google trace dataset for memory and CPU utilization. It would be constructive to endorse the proposed evolutionary neural network approach further by working on other multi-variate resource utilization datasets. Conceptualization, S.M. and M.S. ; methodology, S.M. ; software, S.M. ; validation, M.T., M.S. and A.A.; formal analysis, M.S., M.T. ; investigation, S.M. ; resources, M.S., M.T. ; data curation, S.M., M.T., S.M. ; writing--original draft preparation, S.M. ; writing--review and editing, M.S., M.T., A.A.; visualization, M.T. ; supervision, M.S. ; project administration, A.A., M.S. ; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The datasets used are available publicly. The authors declare to the author. ## Conflicts of Interest:

There is no conflicts of interest to report. ## References

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