## 6 Conclusions  
In this research, our primary objective was to monitor cloud performance through the execution of virtual machines (VMs) under full load conditions. We successfully achieved our aims and objectives and demonstrated the significance of this research in cloud monitoring. To accomplish our goals, we extensively reviewed state-of-the-art cloud monitoring solutions. Based on this review and related work, we proposed a novel solution for monitoring CPU steal time in an OpenStack-based cloud monitoring environment. We emphasized the importance of steal time monitoring as a critical factor in evaluating cloud performance. We implemented our framework in an actual cloud environment to evaluate the effectiveness of our proposed OpenStack-based cloud monitoring system. We established an OpenStack-based cloud with a controller node and a compute node. To measure the performance of our monitoring solution, we introduced a script into the VMs to monitor their CPU steal time. During our experimentation, we observed the monitoring latency before and after applying a full load to the VMs. We also measured the steal time of the VMs. The results indicated that the CPU steal time of a VM increases until it receives the CPU it is waiting for. Moreover, we observed a significant impact on the steal time when running the VMs under a full load compared to running them without limitations. In the absence of a burden, the steal time was found to be minimal.  
Our proposed solution for monitoring the CPU steal time of VMs proved to be highly effective and efficient. We believe this aspect of VM monitoring has been overlooked in previous research studies. The method employed to collect CPU steal time data from VMs is robust and does not impose an excessive burden on the VMs. In summary, our monitoring solution offers a lightweight, efficient, and scalable approach to cloud monitoring. It provides valuable insights into the performance of VMs, particularly with regard to CPU steal time. We implemented it in the private cloud, and in the future, we can implement it in the public and hybrid clouds. We filled a research gap by introducing and successfully implementing this novel monitoring approach.  
Figure 9: Steal time percentage.  
**Author Contributions:** Conceptualization, H.A. and H.J.S.; methodology, H.A., H.J.S. and A.S.; validation, A.O.I., M.A. and M.E.; investigation, A.O.I., M.A. and M.E.; resources, H.A., H.J.S. and A.S.; writing--original draft preparation, H.A., H.J.S. and A.S.; writing--review and editing, all authors; supervision, H.J.S.; project administration, H.J.S., A.O.I., M.A. and M.E.; funding acquisition, M.A. and M.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R383), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R383), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.