## 5 Performance Evaluation
### Profiling Technologies  
A workflow application can be shown as a call graph in an open source connection scrutiny tool which shows that the energy profiling for upload and download of the data. This is also available  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fog Node VM** & **CORE** & **MIPS/CORE** & **Storage (GB)** \\ \hline VM1 & 1 & 200 & 200 \\ VM2 & 1 & 400 & 400 \\ VM3 & 1 & 600 & 600 \\ VM4 & 1 & 800 & 800 \\ VM5 & 1 & 1000 & 1000 \\ \hline
**Public Cloud VM** & **CORE** & **MIPS/CORE** & **Storage (GB)** \\ \hline VM1 & 1 & 500 & 500 \\ VM2 & 1 & 1000 & 1000 \\ VM3 & 1 & 1500 & 1500 \\ VM4 & 1 & 2000 & 2000 \\ VM5 & 1 & 3000 & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: VMs Specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Parameters** & **Values** \\ \hline \(\lambda_{i}\) user arrival time & 5 s \\ Languages & JAVA \\ Workload & A-G, Health-care \\ Experiment time & 12 h \\ Replication Repetition & 12 \\ No. of Mobile devices & 100 to 1000 \\ Location user Mobility & Trajectory function \\ No. of VMs. & 10–200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Workload** & \(W_{i}\) **(byte)** & \(T_{i}^{k}\) **(MI)** & **N** \\ \hline Healthcare & 825 & 5.8 & 700 \\ Augmented Reality & 525 & 4.8 & 800 \\ Business & 525 & 4.8 & 800 \\ 3D-Game & 325 & 2.8 & 1000 \\ Face-Recognition & 725 & 7.8 & 700 \\ Social Net & 625 & 9.8 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Workload analysis.  
at [34]; energy profiling one can use this tool mentioned in [35]. The health-care workflow application is partitioned into local and remote execution. Each task \(v_{i}\) is represented by a node, whereas, each node has exactly two costs (i.e., local execution and cloud execution cost). Further, the application is partitioned under F = 2 speedup factor and available bandwidth = 1 M/B respectively. Whereas, blue nodes are performed locally, and red nodes are offloaded to the cloud for performing. Among this, the proposed JETA method will re-partition the application if the wireless bandwidth B or the speedup factor F diverges.  
Figure 3 shows the individually mobile computing and the fog node energy consumption due to the execution of the benchmark application. JETA manipulates all applications with minimum energy consumption as compared to baseline approaches because of JETA partitions the application at run-time between device and fog in order to minimize energy consumption. Tasks divided into parts and performance of the proposed algorithm is tested.  
Results show that in Figure 4 the proposed algorithm performs better on different speedup factor F and B bandwidth than existing heuristic techniques such as MAUI, ThinkAir, and Clone Cloud in application partitioning. It is also noted that on a higher number of applications, MAUI has nearly the same energy consumption as JETA.  
Figure 5 illustrates the comparison of all three heuristic techniques against JETA in terms of number of tasks completed within a given deadline. Results in Figure 6 show that JETA consumes  
Figure 4: Application partitioning performance based on speed up factor F and bandwidth B.  
Figure 3: Energy consumption of entire mob-cloud.  
less energy as per the number of tasks are increased. MAUI consumes less energy on a low number of tasks, but when the number of the task increased then it consumes more energy. Clone Cloud consumes more energy as compared to all others in this figure. The proposed algorithm performs better on different speedup factor F and B bandwidth than existing heuristic techniques.