# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 6 Simulation and Results  
### Simulation for the Energy-Efficient Scheduling Schemes  
In order to check the effectiveness of the energy-efficient scheduling schemes propose in this paper, we built a simulator based on the power and delay model described above. In addition, we have also implemented the following two algorithms as algorithm benchmarks for comparative analysis. (i) Greedy Algorithm without Backtracking (GAB): This algorithm differs from the task scheduling algorithm based on the greedy algorithm described above in that it does not judge the maximum tolerable delay in line 7 and directly enters line 8, so that the first solution is found greedily. While the solution may exceed the maximum tolerable delay. In order to compare the algorithms more fairly, in the subsequent simulation experiments, if the solution obtained exceeds the maximum tolerable delay, the solution will be multiplied by a factor, increasing the power consumption value of the solution. (ii) Greedy Algorithm with Minimum Delay without Backtracking (GAMDB): the difference between this algorithm and the previous algorithm is that the selection of the greedy algorithm, it will be sorted according to the length of the delay, and the solution with the smallest delay is selected each time, in order to expect to find the solution with the smallest delay, as a comparison group of experiments. The network architecture in the simulation is that every 5 user devices access an edge node, there are 5 edge nodes in total, and all edge nodes access one cloud node in common. In our simulation, the parameters shown in Table 4 which are set for simulation. We pre-configure the coefficient \(\alpha\) of each edge node in Equation (1) to be 10. What's more, we define a penalty coefficient which is 630. It denotes the penalty that exceeding the maximum tolerance. The above four algorithms are implemented separately and compared under the same parameters. We run each of the above algorithms 5000 times and average these results. When the averages are stable, record them as follows. \begin{table}
\begin{tabular}{c c} \hline \hline \(N_{m}\) & The number of running tasks in master node. \\ \(N_{s}\) & The number of running tasks in slave node. \\ \(C_{m}\) & The processing capability of master node. \\ \(C_{s}\) & The processing capability of slave node. \\ \(M_{t}\) & MIPS of the target task. \\ \(T\) & The maximum tolerated time of the target task. \\ \(T_{m}\) & The array of the task remaining time in master node. \\ \(T_{s}\) & The array of the task remaining time in slave node. \\ \(C_{m}^{r}\) & The task remaining MIPS in master node. \\ \(C_{s}^{r}\) & The task remaining MIPS in slave node. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notations. As shown in Figure 4, we implement four algorithms (SSBB, SSBM, GAB, and GAMDB) on our simulator. According to the performance of the above algorithm in the simulator, the following conclusions can be drawn. The optimal solution solved by the SSB has an absolute advantage among the four algorithms. The SSBM algorithm with backtracking effectively finds the sub-optimal solution. When the task transmission size is small, the gap between the optimal solution and the sub-optimal solution is larger, the maximum gap is 8%. With the increasing size of task transmission, the gap is gradually narrowing, eventually less than 1.8%. Because in the process of traversing the solution space, the first feasible solution found using the greedy algorithm may not be the optimal solution, and the branch and bound method is to subtract the impossible branch under the premise of traversing the entire solution space and found the global optimal solution. Since the algorithm GAB uses more edge-cloud nodes and calculates the transmission power consumption. It has a different slope compared with the first two algorithms. Generally, the power consumption of the local calculation is far greater than the transmission power consumption.