# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 4 Experimental Setup
### Results and Analysis  
The performance of the proposed model is compared with an existing cloud service recommendation system using a clustering-based trust degree computation algorithm (CBTDC) [18] and Service Context-Aware QoS Prediction and Recommendation of Cloud Infrastructure Services (SCAQPR) [29]. In the CBTDC algorithm, the limited-service trust measures are considered for the services ranking. The service trust measures are calculated for the available services based on customer feedback. Hence, the algorithm has not focused on the newly arrived services. In addition, the SCAQPR algorithm considered the newly arrived services while preparing the ranked list of services using matrix factorization approach. The services ranking is based on limited QoS factors after computing the missed values of newly arrived services. The proposed work concentrates on 13 service trust factors along with users' feedback to prepare the ranked list of services through SR values as shown in Table 1. Here, the SR  
From Figure 3, it can be seen that the proposed model considers the newly arrived services along with the available services (than existing CBTDC) and the number of service trust factors considered for SR value calculation with respect to the existing SCAQPR is improvised towards to the betterment of an effective service clustering process.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Rank** & \begin{tabular}{l} **Cloud** \\ **Services** \\ \end{tabular} & \begin{tabular}{l} **Proposed** \\ **Model SR** \\ **Value** \\ \end{tabular} & \begin{tabular}{l} **Existing** \\ **CBTDC—SR** \\ **Value** \\ \end{tabular} & \begin{tabular}{l} **Existing** \\ **SCAQPR—SR** \\ **value** \\ \end{tabular} & \
\begin{tabular}{l} **Diff (Proposed— \\ **CBTDC)** \\ \end{tabular} &
\begin{tabular}{l} **Diff** \\ **(Proposed— \\ **SCAQPR)** \\ \end{tabular} \\ \hline \hline \multicolumn{6}{l}{Cloud Services with Trust Factors and Feedback Values} \\ \hline
1 & cs\({}_{8}\) & 0.815 & 0.892 & 0.887 & \(-\)0.077 & \(-\)0.072 \\ \hline
2 & cs\({}_{2}\) & 0.758 & 0.812 & 0.795 & \(-\)0.054 & \(-\)0.037 \\ \hline
3 & cs\({}_{1}\) & 0.747 & 0.853 & 0.761 & \(-\)0.106 & \(-\)0.014 \\ \hline
4 & cs\({}_{10}\) & 0.729 & 0.762 & 0.737 & \(-\)0.033 & \(-\)0.008 \\ \hline
5 & cs\({}_{3}\) & 0.685 & 0.731 & 0.712 & \(-\)0.046 & \(-\)0.027 \\ \hline
6 & cs\({}_{9}\) & 0.634 & 0.676 & 0.643 & \(-\)0.042 & \(-\)0.009 \\ \hline
7 & cs\({}_{6}\) & 0.573 & 0.543 & 0.581 & +0.030 & \(-\)0.008 \\ \hline
8 & cs\({}_{4}\) & 0.519 & 0.407 & 0.492 & +0.112 & +0.027 \\ \hline
9 & cs\({}_{5}\) & 0.435 & 0.407 & 0.437 & +0.028 & \(-\)0.002 \\ \hline
10 & cs\({}_{7}\) & 0.293 & 0.322 & 0.376 & \(-\)0.029 & \(-\)0.083 \\ \hline \hline \multicolumn{6}{l}{Cloud Services with Trust Factors only (Newly Arrived Services)} \\ \hline
11 & ncs\({}_{1}\) & 0.741 (cs\({}_{1}\)) & NA & 0.738 & NA & +0.003 \\ \hline
12 & ncs\({}_{2}\) & 0.291 (cs\({}_{7}\)) & NA & 0.304 & NA & \(-\)0.013 \\ \hline
13 & ncs\({}_{3}\) & 0.632 (cs\({}_{9}\)) & NA & 0.629 & NA & +0.003 \\ \hline
14 & ncs\({}_{4}\) & 0.726 (cs\({}_{10}\)) & NA & 0.719 & NA & +0.007 \\ \hline
15 & ncs\({}_{5}\) & 0.517 (cs\({}_{4}\)) & NA & 0.521 & NA & \(-\)0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the proposed work with the existing works.  
Figure 2: Service trust factors of cloud services.  
The final results of the proposed work are categorized into the clusters, namely the 'Complete trustworthy' cluster group, 'Trustworthy' cluster group, 'Partially trustworthy' cluster group, and 'Untrustworthy' cluster group. Here, the proposed work considered the existing and newly arrived services for the formation of different clusters to prepare and update the Service Collection Repository (SCR), which is shown in Table 3.