## 6 Experimental Work
### Analysis of CS-SR through Execution Time  
The CS-SR algorithm is compared with three existing approaches A.H.P., ENS-SS, and B.W.M. algorithm [70]. Table 5 shows that the CS-SR algorithm has the lowest execution time as compared to the other three algorithms. The execution time of A.H.P., ENS-SS, and B.W.M. increases with the increase in the number of objectives and number of services. CS-SR shows better performance for execution time when compared with other algorithms.  
Tables 5 and 6 represent the execution time for bi-objective [m = 2] and multi-objective [m = 5]. The number of candidate services ranges from [101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 1160] with an increment of 10.  
Table 5 represents the total execution time the CS-SR approach takes in finding the best services according to the user requirements. The execution time increases if the number of candidate services increases. Therefore, we can see from Table 5 and the graph in Figure 3 that for the bi-objective problem, there is a linear increase in the execution time.  
Table 6 represents the total execution time for all four approaches A.H.P., ENS-SS, B.W.M., and CS-SR for m = 5. Each approach is executed separately for the same number of candidate services from [101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 1160], and the execution time is noted for them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & \multirow{2}{*}{**Services**} & \multirow{2}{*}{**Attribute 1**} & \multirow{2}{*}{**Attribute 2**} & \multicolumn{2}{c}{**Fitness Function**} & \multirow{2}{*}{**Rank (TFFs\({}_{i}\))**} \\  & & & & & \\ \hline
1. & S1 & 100 & 10 & \(210\times 770=161,700\) & Rank 1-S1 \\ \hline
2. & S2 & 110 & 6 & \(226\times 348=78,648\) & Rank 4-S2 \\ \hline
3. & S5 & 105 & 9 & \(219\times 456=99,864\) & Rank 3-S5 \\ \hline
4. & S9 & 102 & 8 & \(212\times 550=116,600\) & Rank 2-S9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Integration and ranking of the services.  
Table 6 represents the total execution time the CS-SR approach takes in finding the best services according to the user requirements. The execution time increases if the number of candidate services increases. Therefore, we can see from Table 5 and the graph in Figure 3 that for the multi-objective [M = 5] problem, there is a linear increase in the execution time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Candidate Services** & **AHP** & **ENS-SS** & **BWM** & **CS-SR** \\ \hline
10 & 1.3 & 0.9 & 0.7 & 0.5 \\ \hline
20 & 1.7 & 1.1 & 0.9 & 0.7 \\ \hline
30 & 1.9 & 1.3 & 1.1 & 0.9 \\ \hline
40 & 2.3 & 1.6 & 1.4 & 1.1 \\ \hline
50 & 2.6 & 1.9 & 1.7 & 1.3 \\ \hline
60 & 2.9 & 2.2 & 1.9 & 1.6 \\ \hline
70 & 3.1 & 2.6 & 2.1 & 1.8 \\ \hline
80 & 3.4 & 2.9 & 2.5 & 2.1 \\ \hline
90 & 3.6 & 3.2 & 2.8 & 2.4 \\ \hline
100 & 3.8 & 3.4 & 2.9 & 2.6 \\ \hline
110 & 4.2 & 3.7 & 3.1 & 2.8 \\ \hline
120 & 4.4 & 3.9 & 3.3 & 2.9 \\ \hline
130 & 4.7 & 4.2 & 3.6 & 3.2 \\ \hline
140 & 5 & 4.6 & 4 & 3.6 \\ \hline
150 & 5.3 & 4.9 & 4.4 & 3.8 \\ \hline
160 & 5.6 & 5.3 & 4.7 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Execution time [M = 5].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Candidate Services** & **AHP** & **ENS-SS** & **BWM** & **CS-SR** \\ \hline
10 & 0.85 & 0.8 & 0.7 & 0.5 \\ \hline
20 & 1.4 & 1 & 0.9 & 0.7 \\ \hline
30 & 1.7 & 1.2 & 1 & 0.8 \\ \hline
40 & 2 & 1.4 & 1.2 & 1 \\ \hline
50 & 2.4 & 1.7 & 1.5 & 1.2 \\ \hline
60 & 2.5 & 1.9 & 1.7 & 1.4 \\ \hline
70 & 2.8 & 2.1 & 1.9 & 1.7 \\ \hline
80 & 3.1 & 2.4 & 2.2 & 1.9 \\ \hline
90 & 3.3 & 2.7 & 2.4 & 2.1 \\ \hline
100 & 3.6 & 2.9 & 2.7 & 2.4 \\ \hline
110 & 3.8 & 3 & 2.9 & 2.6 \\ \hline
120 & 4 & 3.2 & 3.1 & 2.8 \\ \hline
130 & 4.2 & 3.5 & 3.3 & 3.1 \\ \hline
140 & 4.4 & 3.7 & 3.6 & 3.2 \\ \hline
150 & 4.6 & 4 & 3.9 & 3.5 \\ \hline
160 & 4.7 & 4.2 & 4.1 & 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time [M = 2].