## 4 Simulation Results and Discussion  
The simulation environment used is MATLAB (R2019a) running on an Intel Core i7 processor with a clock rate of 3.4 GHz. In the simulation, all techniques, except TOT, divide a task of size \(m\) in six components, which are executed in a sequence on UE or through MES. All random variables are independent for different components. The number of raw data that is used to generate the training dataset is 40,000, which means that we execute 40,000 tasks of different sizes, uniformly distributed in [0.1 1.5] GB, independently through our proposed algorithm and save the output with minimum cost as labels for corresponding input data. The \(f_{ui}\), \(p_{ui}^{t}\), \(\lambda_{i}\), \(k_{i}\), and \(r_{i}\) are taken as uniformly distributed in [0.1, 1] GHz, [0.8 1.25] Watt, [3 800] m, [1 256], and [0 16], respectively. The number of CPU cycles to process one bit of data is taken as 737.5 cycles/bit. The effective switching capacitance factor is taken as \(10^{-27}\). The simulation parameters are given in Table 3. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(B\) & 0.5 MHz & \(R\) & 256 \\ \(E_{max}\) & 800 J & \(p_{ui}^{t}\) & 1.2 W \\ \(K\) & 16 & \(p_{ui}^{t}\) & 0.8 W \\ \(d_{max}\) & 300 s & \(\delta_{1}\) & 0.6 \\ \(\gamma\) & 200 MB & \(\delta_{2}\) & 0.4 \\ \(\epsilon\) & \(10^{-27}\) & \(\delta_{3}\) & 0.5 \\ \(\eta\) & 737.5 cycles/bit & \(\delta_{4}\) & 0.3 \\ \(N_{o}\) & \(-174\) dBm/Hz & \(\delta_{5}\) & 0.1 \\ \(\theta\) & 3 & \(\delta_{6}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. After getting the training dataset, we also generate a test dataset of size 10,000 to check the accuracy of trained DNN for unseen data. There are 21 neurons in the input layer; one for task size, one for division resolution, one for number of components per tasks, six for random distances during mobility of UEs in execution of each component, six for subcarriers during the execution of each component, and six for CPU cores assigned to each components. Similarly, six neurons for offloading policy and six neurons for partitioning are reserved in the output layer. The ReLU and Softmax activation function are used for the two hidden layers and the output layer, respectively. Figure 2 presents the comparison of energy consumption of a UE with a varying task size. The energy consumption of CEDOT is minimum because it considers the suitable component size along with the offloading policy having minimum cost. For different component sizes, the energy consumption varies with different offloading policies. However, the proposed technique selects the offloading policy with minimum cost of portioning and offloading. Figure 3 shows the service delay versus task size. Here, we can observe better performance of CEDOT because of the proposed comprehensive cost function. The benchmark techniques do not consider the delay and energy consumption due to task-division for partial offloading in the cost function, which is not a realistic approach. However, the proposed technique considers various types of realistic delays in the cost function and finds minimum values for various types of delays. The benchmark techniques select components with random sizes and also ignore them in the cost function. Figure 3: Service delay for different task sizes. Figure 2: Energy consumption for different task sizes. Figure 4 shows the normalized cost of all the techniques with a varying size of tasks. The cost of the proposed technique is minimum because it selects the minimum value among \({}^{m}\!Z_{n}2^{n}\) possible values of the cost. However, the other benchmark techniques ignore this fact and select the cost value depending only on time or energy consumption. Figure 5 presents the size of training dataset along with a different number of components per task. As the number of components per task increases, the complexity of the decision boundaries increases.