# 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  
By utilizing real-world task data generated from a large-scale supercomputing facilities, we evaluate performance of a resource scheduling metaheuristic. In this subsection we present results that the WOA-AEFS obtained when a dataset from a real computing environment was used. The same resource scheduling model and the same dataset was utilized in [75]. The tasks utilized in the conducted simulations were generated from NASA Ames iPSC/860 log [99] in [PERSON]'s Parallel Workloads Archive (PWA). The NASA iPSC is located in the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center. It comprises the data of a sanitized account record for the 128-node iPSC/860 for three months (October 1993-December 1993). The log contains data about 42264 tasks and 128 resources (CPUs). The data was retrieved from the the workload trace log file _NASA-iPSC-1993-3.swf_ from the URI: [[https://www.cse.huji.ac.il/labs/parallel/workload/1_nas_ipsc/](https://www.cse.huji.ac.il/labs/parallel/workload/1_nas_ipsc/)]([https://www.cse.huji.ac.il/labs/parallel/workload/1_nas_ipsc/](https://www.cse.huji.ac.il/labs/parallel/workload/1_nas_ipsc/)). From this log we retreived the task ID, length of each task expressed in MIPS and the requested processing elements (PEs). For the purpose of simulations, we generated cloud infrastructure by using the CloudSim framework. In all simulations we utilized 10 homogeneous VMs, that are located on two physical hosts within one data center. The data center was created with default CloudSim characteristics. The characteristics of hosts and VMs used in experiments are summarized in Tables 6 and 7, respectively. In order to evaluate the robustness and scalability of the proposed WOA-AEFS in terms of the number of tasks, we performed experiments with a small task set (from 100 to 600 tasks) and with a large task set (with 1000 and 2000 tasks). We adjusted the basic WOA-AEFS parameters as follows: size of population to 30 (\(N=30\)) and maximum number of iterations in one run to 1000 (\(maxIter=1000\)). Parameter \(limit\) was set to 33 (\(round(1000/30)\)), while the dynamic parameters \(\vec{a}\), \(\vec{e}\)r, and \(\alpha\) were adjusted during the course of one run by expressions (28), (33) and (36), respectively. The other WOA-AEFS parameters were adjusted as shown in Table 3.  
The same CloudSim environment, as well as the parameters for population size and maximum iterations were utilized in [75]. In this way, we wanted to perform a more objective comparative analysis between our proposed WOA-AEFS and other approaches that were presented in [75]. We executed tests for eight instances, with 100, 200, 300, 400, 500, 600, 1000, and 2000 number of tasks. For each experiment instance, the WOA-AEFS was executed in 100 independent runs, and we calculated the averages of the makespan (MS) objective and the DI (Equation (11)). Details of the resource scheduling model that was employed can be retrieved from Section 3.1. We note that we have also adapted the basic WOA and performed experiments against this model, because we wanted to see comparison between the basic and hybridized version. We first wanted to compare the performance of WOA-AEFS with the basic WOA. Comparative analysis between WOA-AEFS and the original WOA for both sets, small number of tasks (from 100 to 600) and large number of tasks (1000 and 2000) is shown in Figure 2.  
From the presented diagram, it is clear that the WOA-AEFS outperformed WOA in all test instances, except in the simulation with 400 tasks. In this case, the original WOA obtained slightly better MS value (around 1%). The most significant performance difference can be noticed in simulations with 600 tasks, where WOA-AEFS outperformed original WOA by more than 25%.