# Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling
## 4 Evaluation
### Performance Results  
Figure 3 shows the numbers of migrations of the previous and proposed methods. When the number of users is small (one or two users), the difference between the previous and proposed methods is relatively small. However, as the number of users increases, the difference between the previous and proposed methods becomes greater. Specifically, when the number of users is 5, the numbers of migrations of the previous and proposed methods are 14 and 19, respectively, with a difference of 5. If the number of users is 10, the numbers of migrations of the previous and proposed methods are 19 and 31, respectively, with a difference of 12. This signifies that the proposed method optimizes the performance of tasks by migrating them appropriately. Figure 4 shows the network usage of the previous and proposed methods. Note that the total network usage is the sum of that of the devices and migration, and the baseline data in the figure indicate when the number of users is 1. The network usage of migrations accounts for most of the total network usage, which is affordable in recent high-bandwidth network environments. Since the proposed method introduces more live migrations than the previous method, the network usage of the proposed method generates more network traffic than the previous method. In a multi-user environment (when the number of users is 5), the difference in network usage between the previous and the proposed methods is about 1 GB. Although the proposed method incurs more network traffic, it improves the overall performance in terms of downtime and the reliability of tasks. To figure out the performance of time-related metrics, we measure the average downtime and the total time of migration (c.f., Figure 5). As in Figure 4, we refer to the baseline performance in Figure 5 for comparison purposes. In terms of the average downtime, the proposed method outperforms the previous method. Specifically, the proposed method has only about 19% of the downtime seen in the previous method. This means that our method is more than five times better than the previous method in terms of downtime. When we compare the total time of migration, our method takes longer than the previous method, since our method incurs more live migrations than the previous one. Note that the proposed method does not generate meaningful overhead as the number of users increases. As for the task reliability, we measure the percentages and the numbers of Tuples lost for the previous and proposed methods (c.f., Figure 6). Figure 6a shows the percentages of tuples lost. For the previous method, the percentages of tuples lost are 3.580%, 3.769%, 7.399%, 8.002%, 7.919%, and 6.194% when the numbers of users are 1, 2, 3, 4, 5, and 10, respectively. For the proposed method, the percentages of tuples lost are 0.027%, 0.033%, 0.038%, 0.041%, 0.041%, and 0.041% when the numbers of users are 1, 2, 3, 4, 5, and 10, respectively. Figure 3: The number of migrations with the number of users. Figure 4: Network traffic usage in MB. (**a**) The number of users: 2. (**b**) The number of users: 3. (**c**) The number of users: 4. (**d**) The number of users: 5. Figure 5(b) shows the numbers of tuples lost. For the previous method, the numbers of tuples lost are 87,078, 136,802, 376,588, 547,976, 666,795, and 979,050 when the numbers of users are 1, 2, 3, 4, 5, and 10, respectively. For the proposed method, the numbers of tuples lost are 762, 1416, 2637, 4029, 4875, and 13,661 when the numbers of users are 1, 2, 3, 4, 5, and 10, respectively. Compared to the previous and proposed methods, the proposed method generates 0.875%, 1.035%, 0.700%, 0.735%, 0.731%, and 1.395% of the tuples lost compared with the previous method when the numbers of users are 1, 2, 3, 4, 5, and 10. On the whole, the proposed method improves the overall performance for cloud tasks of IoT and IIoT devices in fog computing environments by optimizing the average downtime and the reliability of cloud tasks while generating affordable network traffic for live migrations.