# Reducing Dynamic Power Consumption in Mixed-Critical Real-Time Systems
## 6 Performance Evaluation
### Energy Consumption Results  
We present energy consumption for different task sets as shown in Figure 6a-d. We measure the average value of 1000 task sets. The figure presents energy consumption as a function of system utilization for different probabilities. As shown in the figure, the proposed approach achieves better minimum energy consumption compared to that of existing approaches for the same task set. The main reason of minimum energy consumption is due to the task utilization at low and high criticality modes. The figure further shows that when the probability of high-criticality mode is increased, the impact of energy consumption gradually increases from 0.01 to 0.09. As shown in Figure 6c, the minimum energy consumption depends on the probability values for task utilization U = (0.2, 0.25, 0.3, 0.35, 0.4, 0.45).  
We also present the impact of average \(x\) on energy minimization in Figure 7. We consider the same value of \(x\) for both previous and proposed approaches. When the value of utilization is increased by 0.35, the proposed approach achieves significant improvement in the performance. The impact of \(x\) in the probabilities is shown in Figure 7a. When the utilization is between 0.2 and 0.25, the average \(x\) is 0.4 but when the utilization is increased up to 0.35 and the value of \(x\) is increased by 0.56. When the utilization is between 0.35 and 0.4, then the average value of \(x\) goes to 0.65. This implies that in HI-criticality mode the energy consumption is not affected when we increase the value of \(x\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline CPU Frequency levels & 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 \\ \(\begin{matrix}\text{U}_{LO}^{LO}\\ \text{U}_{LO}^{HI}\end{matrix}\) & 0.3, 0.35, 0.4, 0.45, \\ \(\begin{matrix}\text{Low-to-high rate ($r$)}\end{matrix}\) & 1.5, 2.0, 2.5, 3.0 \\ Low-criticality tasks & 2 \\ High-criticality tasks & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
Figure 8 shows energy consumption as a function of different ratios of low- and high-computation times. The figure considers different values of \(r\) ranging from 1.5 to 3. The ratio between low-critical and high-critical execution time in the sequence in order to observe its effects on the scheduling of mixed-criticality tasks. As shown in Figure 8, the increasing ratio also leads to an increase in the average energy consumption. When the ratio is 1.5, the values of average energy for proposed and previous approaches are 0.082 and 0.136, respectively. Similarly, when the probability is between 0.6 to 0.4, the proposed approach minimizes energy consumption as compared to that of the previous approach as shown in Figure 8b. It is concluded that an increase in the ratio leads to increase in the average energy consumption of the mixed-criticality task sets.  
Figure 6: Average energy results.  
Figure 7: The impact of \(x\).  
The result in Figure 9 shows the impact of different task sets in mixed-criticality systems. The figure presents the average energy as a function of seven task sets, i.e., (1 LO/6 HI, 2 LO/5 HI, 3 LO/4 HI, 4 LO/3 HI, 5 LO/2 HI, 6 LO/1 HI) ranging from low to high critical modes. It is observed that the average energy is increasing for the average number of 1000 task sets.  
In Figure 10, the average energy consumption is presented for different frequency intervals. The figure shows the effects of the task-sets frequencies on minimum energy consumption. In the range between 0.4 and 0.5, we generate random task sets utilization for the sufficient number of tasks. When the frequency interval is between 0.05 and 1, the proposed approach outperforms the previous approach approach. Figure 9(b) shows that when the frequency interval is between 0.05 and 0.1, the value of \(x\) decreases. It is concluded that the proposed approach achieves a lower value of \(x\) compared to that of the previous approach.