# 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  
In the second resource scheduling simulation, we used a multi-objective model with performance and budget constraints. In this experiment, the objective was to minimize the MS along with the total cost of CPU and memory resources. For more information about the model, please refer to Section 3.2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{8}{c}{**Number of Tasks**} \\ \hline  & 100 & 200 & 300 & 400 & 500 & 600 & 1000 & 2000 \\ WOA & **2730** & 4122 & 6541 & **8567** & 11,005 & **15,349** & **35,152** & 118,782 \\ WOA-AEFS & 2855 & **3983** & **6374** & 8750 & **10,730** & 15,765 & 36,110 & **109,351** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative analysis of the CPU time in simulations with NASA iPSC real trace data set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Tasks** & **PSO** & **MSDE** & **WOA** & **WOA-AEFS** \\ \hline
100 & 0.974118 & 0.972773 & 1.004538 & **0.935002** \\
200 & 1.375412 & 1.138444 & 1.337259 & **1.373259** \\
300 & 1.051463 & **0.905947** & 1.032713 & 0.983205 \\
400 & 1.050034 & **0.878397** & 1.063955 & 0.883302 \\
500 & 1.077579 & 0.942521 & 1.311571 & **0.941056** \\
600 & 1.097044 & 0.983239 & 1.270371 & **0.976200** \\
1000 & **1.013558** & 1.040253 & 1.093521 & 1.0387226 \\
2000 & **0.974118** & 1.011911 & 0.991087 & 0.9835211 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative analysis of the DI indicator in simulations with NASA iPSC real trace data set. For this experiment, we used an artificial dataset. The simulation environment is generated in the CloudSim 3.0.3 platform, as in experiments with the real data set. For each algorithm run, we generated a random environment with 50 heterogeneous VMs implemented under hosts located in data centers. We have considered varying the number of heterogeneous tasks (from 100 to 500) with different lengths. Moreover, we have also considered various tasks arrival rates that were set to 10 and 40. The same simulation environment was utilized in [64]. The characteristics of tasks, VMs, hosts and data centers are shown in Tables 11 and 12. We have adjusted the basic WOA-AEFS parameters as in the experiments with the real dataset: the size of population was set to 30 (\(N=30\)) and the maximum number of iterations in one run was adjusted to 1000 (\(maxIter=1000\)). The parameter _limit_ was set to 33 (\(round(1000/30)\)), while the dynamic parameters \(\vec{a}\), _eir_ and \(\alpha\) were adjusted during the course of one run by the expressions (28), (33), and (36), respectively. The other WOA-AEFS parameters were adjusted as shown in Table 3.  
Similarly as in [64], we have separately evaluated MS, cost, and deadline violation and performed comparative analysis with Min-Min and first come first serve (FCFS) heuristics, as well as with cuckoo search particle swarm optimization (CPSO) and performance budget ACO (PBACO) metaheuristics. Moreover, to more precisely evaluate performance of our proposed WOA-AEFS, we have also included original WOA in the comparative analysis. Simulation results for Min-Min and FCFS heuristics and CPSO and PBACO metaheuristics were taken from [64], while we have performed simulations for the basic WOA approach using our own implementation in Java. In [64], implementation of state-of-the-art CPSO was shown. For each simulation, WOA and WOA-AEFS are executed in 100 independent runs and average results are reported. For each run, a new random cloud environment was generated.