## 5 Performance Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of mobile devices & 100\(\sim\)1000 \\ Number of Small Base Stations (SBS) & 16 \\ VMs per Mobile/MEC server/Cloud & 1/4\(\sim\)8/4 \\ VM processor speed (GIPS) per & \\ Mobile/MEC server/Cloud & 2/10/100 \\ Active/Idle period (sec) & 40/20 \\ Average Upload/Download data size (KB) & 1500/25 \\ Average size of the task (GI) & 2\(\sim\)20 \\ Delay Sensitivity (\%) & 0.1\(\sim\)0.9 \\ Inter-arrival time of tasks (s) & 2 \\ Propagation delay (ms) & 3 \\ Application type & Augmented Reality \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation parameters. From the analysis in Figure 8b, it is observed that, when the average task size is between 2 and 10 GI, the local SBS-MEC offloading and the proposed FCTO scheme provide almost similar results, while the WOTO scheme provides lower server utilization compared with the other schemes. Meanwhile, if the task size is 16 GI, then the average server utilization for local SBS-MEC offloading, WOTO, and the proposed FCTO schemes are 8.43%, 1.23%, and 10.52%, respectively. Thus, the proposed FCTO scheme accomplishes a server utilization that is 2.09% more than that of local SBS-MEC offloading and 9.29% more than that of the WOTO scheme. Therefore, from the review, it can be concluded that our introduced offloading scheme will provide more server utilization than others. This is because our proposed system can make a dynamic decision as it analyzes the CPU usage of all VMs on the SBS-MEC servers and the VM with the least CPU usage is selected to compute the task. On the contrary, the WOTO scheme requires offloading some tasks to the remote cloud for processing. Moreover, to validate the significance of the proposed FCTO scheme, we conducted another experiment considering the average task failure rate and task completion time with respect to the number of mobile devices is shown in Figure 9a and Figure 9b respectively, where both the \(x\)-axes indicate the scalability of the mobile device and the \(y\)-axes represent the average task failure rate and task completion time, respectively. In these experiments, we have assigned eight VMs in each MEC server. In case of a lightly loaded system, for example, if the number of mobile devices is up to 200, the performance of the above-mentioned three approaches will provide a similar result and the average task failure rate will be approximately 0, which is represented in Figure 9a.The average task failure rate tends to increase in all three scenarios, with an augmentation of the number of mobile devices; however, when the number of mobile devices is more than 200, the proposed FCTO scheme will provide less task failure compared with the other two schemes, as these tasks are distributed and offloaded easily to the neighboring SBS-MEC server. Meanwhile, the local SBS-MEC offloading scheme cannot handle more tasks when the number of mobile devices is more, and, due to the congestion of VMs in the local SBS-MEC server, the WOTO scheme sends the incoming task to the remote cloud. Because of the WAN bandwidth, the average task failure will be increased in the WOTO scheme. From Figure 9b, it can be observed that, when the system is heavily loaded due to the growing number of mobile devices, the completion time of the tasks, as well as network delay, will be increased in all scenarios because of the congestion. For example, when the number of mobile users is 600, then the average task completion time for local SBS-MEC offloading, WOTO, and the proposed FCTO schemes are 1.54, 2.72, and 0.68 s, respectively. Thus, the proposed FCTO scheme reduces the task completion time by 0.86 s when compared with the local SBS-MEC offloading and by 2.04 s when compared with WOTO scheme. Therefore, by comparing with the other two approaches,  
Figure 8: Performance analysis: (**a**) average task completion time for the different task sizes; (**b**) the effect of different task sizes for server utilization.