# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 5 Practical Simulations, Analysis, and Discussion
### Cloud Computing Resource Scheduling Simulations
#### 5.2.1 Simulations with Real Data Set
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Host Identifier** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Host2} & RAM & 3 (units: GB) \\  & CPU type & Intel Core i7 Extreme Edition 3960X \\  & Number of cores (PEs) & 6 \\  & CPU ability & 177,730 (units: MIPS) \\  & storage capacity & 1 (units: TB) \\  & bandwidth & 10 (units: Gbps) \\  & VMs scheduling policy & space-shared \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(Cont.\) \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline number of VMs & 10 \\ CPU processing power & 9726 (units: MIPS) \\ RAM memory & 512 (units: MB) \\ bandwidth capacity & 1000 (units: Mbps) \\ storage capacity & 10 (units: GB) \\ task scheduling policy & time-shared \\ VMM (Hypervisor) & Xen \\ Operating System & Linux \\ number of CPUs & 1 \\ CPU type & Pentium 4 Extreme Edition \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CloudSim VMs configurations for simulations with a real data set. Convergence speed graph obtained for WOA-AEFS and WOA in test instance with 200 tasks is given in Figure 3.  
In order to further assess the robustness of WOA-AEFS, we performed comparative analysis with the moth search differential evolution (MSDE) and PSO algorithms, that are presented in [75]. We excluded from comparative analysis heuristics due to the fact that heuristics like round robin (RR) generate high MS value. In [75], results for the original moth search (MS) are also presented. However, we have also excluded this algorithm from comparative analysis, since the MSDE significantly outperforms the basic MS algorithm. However, in order to see how basic WAO compares to other state-of-the-art approaches, it was included in comparison. We note that the results of the basic WOA were also shown in [75]. However, we did not take results from this paper, instead we performed simulations with our own WOA implementation. Results with smaller numbers of tasks (from 100 to 600) and with large task sets (1000 and 2000) are shown in Figures 4 and 5, respectively. Figure 3: Convergence speed graph WOA-AEFS vs. WOA using NASA iPSC real trace for 200 tasks. Figure 2: Comparative analysis—WOA-AEFS vs. WOA using NASA iPSC real trace for small and large number of tasks. Finally, we wanted to perform comparative analysis between WOA-AEFS and WOA, MSDE and PSO by using the DI indicator (Equation (11)). For more information, please refer to Section 3.1. Comparative analysis of the DI is shown in Table 9 (results for MSDE and PSO were taken from [75]). The best results from each category of test instances are marked bold. Based on the analysis of DI indicator results shown in Table 9, it can be stated that, on average, the WOA-AEFS obtained the best results. The PSO proved to be capable of establishing effective load balancing between VMs in environments with large number of tasks (1000 and 2000), and in these simulations this approach showed the best performance. In simulations with 300 and 400 tasks, the MSDE outperformed WOA-AEFS, as well as other algorithms. Our proposed WOA-AEFS showed superior quality of results in small test instances with 100, 200, 500, and 600 numbers of tasks. As a general conclusion, in comparative analysis with both a small and large number of tasks, the WOA-AEFS shows better performance than other outstanding metaheuristics. The second best is the MSDE approach, while the original WOA and the PSO on average obtained similar results. Considering the possibility of implementing metaheuristics-based resource scheduling techniques in a real cloud environment, CPU time required for obtaining promising solution represent an important indicator. Since in [75], details of the computation platform are not given, we could not compare the required CPU time of the WOA-AEFS with the CPU time for the MSDE and PSO. However, since we have implemented and tested both original WOA and the WOA-AEFS on the same computing platform, in this case we were able to perform such comparison.