# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 5 Practical Simulations, Analysis, and Discussion
### Cloud Computing Resource Scheduling Simulations
#### 5.2.2 Resource Scheduling Simulations with Artificial Data Set
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity ame** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{VM} & Number of VMs & 50 \\  & RAM & 512 (units: MB) \\  & CPU processing power & 1860, 2660 (units: MIPS) \\  & storage capacity & 1 (units: GB) \\  & bandwidth capacity & 1000 (units: Mbps) \\  & Task scheduling policy & time-shared \\  & VMM (Hypervisor) & Xen \\  & Operating System & Linux \\  & Number of CPUs & 1 \\ \hline \multirow{4}{*}{Host} & RAM & 2 (units: GB) \\  & storage capacity & 10 (units: GB) \\ \cline{1-1}  & bandwidth capacity & 1 (units:Gbps) \\ \cline{1-1}  & VMs scheduling policy & space-shared \\ \hline \multirow{2}{*}{Data center} & Number of data centers & 10 \\ \cline{1-1}  & Number of hosts & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: CloudSim VM, hosts and data centers configurations for simulations with artificial data set. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline number of tasks & 100–500 \\ tasks length & 400–1000 (units: MI) \\ file size & 200–1000 (units: MB) \\ output size (memory) & 20–40 (units: MB) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: CloudSim tasks characteristics for simulations with real data set. Makespan Analysis  
First we show comparative analysis between the WOA-AEFS and other above mentioned heuristics and metaheuristics for the MS value. The result diagrams along with the data table when the tasks arrival rate was set to 10, is shown in Figure 6.  
According to the presented results, on average WOA-AEFS obtained the best value for the MS indicator compared to all other approaches included in analysis. It achieved better performance than the CPSO metaheuristics, which proved to be the second best, and much better than the original WOA. The WOA-AEFS established better results than CPSO algorithm in test instances with 100, 300, and 500 tasks. In benchmarks with 200 and 400 tasks, CPSO established slightly better MS value than our proposed WOA-AEFS. Moreover, based on the presented results, it can be concluded that the WOA-AEFS significantly outperformed basic WOA in all test cases. The largest performance increase could be observed in the benchmark with 100 tasks, where the WOA-AEFS outperformed WOA by around 19%. In tests with 200, 300, 400 and 500 tasks, WOA-AEFS improved the MS value of the original WOA by around 5%, 15%, 13%, and 9%, respectively. Finally, it can be also concluded that the basic WOA performs similarly to PBACO metaheuristics. The result diagrams along with the data table when the tasks arrival rate was set to 40 is shown in Figure 7.  
In the experiments with tasks arrival rate of 40, the WOA-AEFS obtained better results than state-of-the-art CPSO in four out of five test instances. Only in tests with 500 tasks, did CPSO outperform WOA-AEFS by an insignificant 2%. The most significant performance difference between these two algorithms can be observed in the test with 100 tasks, where the WOA-AEFS obtained better results by around 19%. Figure 6: Comparative analysis—WOA-AEFS vs. others using artificial dataset (makespan at arrival rate of 10). As in simulations with a task arrival rate of 10, the WOA-AEFS proved to be the best metaheuristic, while the CPSO took second place. In all test instances, WOA-AEFS completely outperformed the basic WOA approach, which showed similar results to PBACO. As expected in simulations with both task arrival rates of 10 and 40, heuristics algorithms showed the worst performance. However, the Min-Min heuristics could compete with metaheuristic approaches in some test instances. For example, the Min-Min heuristics obtained a better MS value than the PBACO in tests with 100 and 200 tasks and arrival rates of 10 and 40. On the other hand, Min-Min heuristics achieved, on average, two times better performance than FCFS heuristics.