## 4 Results
### GoCJ Workload  
The workload used in this study was \"GoCJ_Dataset_1000.txt\" from the GoCJ workload task set [38]. The GoCJ workload is a popular dataset that recreates the workload features of the Google Cluster workload. Since its publication, it has found use in many works related to task scheduling [9; 13]. The task length in this dataset varies from 15,000 MI to 900,000 MI. A deadline file was generated using the workload data and for priority input the six priority sets used for synthetic workload were used. The simulation was run six times using the six combinations of inputs, with the final result being obtained by averaging the results of all six input sets.  
The results for total number of VMs used for task scheduling are presented in Figure 11a. The proposed approach was found to require 103.82%, 103.82%, and 103.82% fewer VMs than the FCFS-EDF, SJF-EDF, and Random-EDF approaches, respectively. The graphs for DoI are presented in Figure 11b and show that, again, the proposed approach is superior to the other three approaches. For this measure, the improvements were found to be 161.28%, 69.45%, and 248.32% against the FCFS-EDF, SJF-EDF, and Random-EDF approaches, respectively. Considering the results for makespan presented in Figure 11c, it was found that the proposed approach required 14.17%, 30.97%, and 2.89% more makespan compared to FCFS-EDF, SJF-EDF, and Random-EDF, respectively. As shown by the graphs presented in Figure 11d, it was found that FCFS-EDF, SJF-EDF, and Random-EDF were 4.72% 13.03%, and 4.74% more expensive compared to the proposed approach, respectively.  
For the performance measure concerning average completion time for high-priority tasks, as shown in Figure 12a, the three compared approaches required 104.76%, 100.15%,and 119.59% more time than the proposed approach, respectively. However, when considering the average completion time for low-priority tasks, the proposed approach was found to require 52.49%, 53.34%, and 49.19% more time compared to FCFS-EDF, SJF-EDF, and Random-EDF, respectively. The graphs for this measure are presented in Figure 12b. Moving to the results for average waiting times for high-priority tasks, as presented in Figure 12c, the proposed approach had a clear advantage over the compared approaches. Compared to the proposed approach, the FCFS-EDF, SJF-EDF, and Random-EDF approaches took 2399.69%, 2303.57%, and 2734.45% more waiting time, respectively. The next metric under consideration was average the waiting time for low-priority tasks, and for this metric, the proposed approach required 67.28%, 68.37%, and 63.05% more time compared to FCFS-EDF, SJF-EDF, and Random-EDF, respectively. The graphs for this metric are presented in Figure 12d.  
The results for the average waiting times for all tasks are presented in Figure 13a. For this metric, it was seen that the proposed approach required 55.38%, 56.45%, and 51.34% more waiting time overall compared to FCFS-EDF, SJF-EDF, and Random-EDF, respectively. The graph for the total SLA violation time for high-priority tasks is presented in Figure 13b. For this measure, the proposed approach achieved a perfect result, with zero SLA violation time for high-priority tasks. Therefore, it is clearly better than the compared approaches. Figure 13c presents the results for the total number of SLA violations. For this measure, the proposed approach faced a 42.01%, 40.70%, and 40.88% higher total number of SLA violations compared to FCFS-EDF, SJF-EDF, and Random-EDF, respectively. However, as evidenced by the next measure, all the SLA violations faced by the proposed approach were for low-priority tasks. Finally, with respect to the total number of SLA violations that occurred for each approach, as shown by the graph in Figure 13d, the proposed approach faced no SLA violations for high-priority tasks and, thus, is better than the compared approaches for this measure.