# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 5 Conclusions and Future Enhancements  
In a multi-cloud environment, the validation and verification of cloud services play a crucial role in recommending appropriate services that meet user requirements and ensure service quality. The proposed work introduces an intelligent cloud broker (ICB) designed to address the dynamic needs of cloud users by calculating service ranking (SR) values based on multiple service trust factors derived from existing services. Additionally, the ICB efficiently accommodates newly arrived services by referencing the SR values of similar existing services, thereby ensuring adaptability and continuity in service recommendations. To enhance the accuracy of SR value calculation, the model incorporates thirteen distinct service trust factors, leading to improved performance compared to existing approaches. The experimental evaluation, conducted on a set of selected cloud services, demonstrates the model's effectiveness in service ranking and recommendation. However, the current implementation is limited by the scope of the dataset and static evaluation parameters. As a direction for future work, the model can be enhanced by integrating advanced machine learning algorithms that can learn and adapt automatically to large-scale, real-time, and dynamic cloud service datasets. This integration will further enable the intelligent cloud  
Figure 3: Comparison of proposed work with existing works.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Clusters** & **Cloud Services** \\ \hline Complete trustworthy cluster group & CS\({}_{8}\), CS\({}_{2}\) \\ \hline Trustworthy cluster group & CS\({}_{1}\), CS\({}_{10}\), CS\({}_{3}\), CS\({}_{9}\), CS\({}_{6}\), CS\({}_{4}\), \(nCS_{1}\), \(nCS_{3}\), \(nCS_{4}\), \(nCS_{5}\) \\ \hline Partially trustworthy cluster group & CS\({}_{5}\) \\ \hline Untrustworthy cluster group & CS\({}_{7}\), \(nCS_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clusters with the cloud services of the proposed model.  
broker to scale effectively and maintain high recommendation accuracy in practical multi-cloud scenarios.  
**Author Contributions:** R.N.: conceptualization of the research framework; overall supervision; manuscript review and editing; ensured alignment with cloud computing standards and trust modeling principles. V.P.: system design and implementation; data collection and experimentation; preparation of graphs and tables; contributed to drafting the initial manuscript. R.T.: technical guidance on ranking heuristics and trust factor integration; contributed to methodology refinement and literature review; assisted in manuscript revisions. J.A.P.: formal analysis and validation of the proposed model; coordinated peer review responses and manuscript finalization; served as the corresponding author and provided academic supervision throughout the research. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** The authors would like to acknowledge the infrastructure support provided by Sri Venkateswara College of Engineering, Sriperumbudur, Tamil Nadu, India; Vellore Institute of Technology, Vellore, Tamil Nadu, India, which were essential for the successful completion of this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.