5.3 Experiments and Results
5.3.2 Experimental Results
Experiment 1- Impact of utilization: In this experiment, we have varied the system load on a heterogeneous system withfmaxHP = 1.0 and fmaxLP = 0.8. Figure 5.7 depicts the effect of utilization on normalized energy consumption. The values ofn andk are set to 10 and 4, respectively. Here, the utilization shown in the X-axis is considered as regards the power-efficient LP core. With reference to the energy consumption of SlowerP at U = 1.0, we normalize the obtained results. It may be observed from Figure 5.7 that as expected, the energy consumption of both FENA-SCHED and SlowerP increases when system load increases. This is because for a given number of tasks, the average individual task utilization increases with an increase in the total utilization and hence, task execution time increases. This reduces the idle times of both processing cores, leads to higher energy consumption. It may also be observed that in all system load conditions, our proposed scheme FENA-SCHED outperforms SlowerP. This is due to the fact that FENA-SCHED reserves only a fixed amount of backup slots onHP core with respect to the number of faults to be tolerated and allows backup-backup (BB) overloading within these backup slots. For example, when U = 0.6, FENA-SCHED and SlowerP yield an overall energy consumption of 82.41mJ and 110.21mJ, respectively. When utilization increases toU = 1.0, their overall energy consumptions become 127.8mJ and 174.36mJ, respectively.
5.3 Experiments and Results
0 20 40 60 80 100 120
0.4 0.5 0.6 0.7 0.8 0.9 1.0
Normalized energy consumption (%)
Utilization
FENA-SCHED SlowerP
Figure 5.7: Impact of utilization
Experiment 2- Impact of number of faults: In this experiment, we have varied the number of faults (k) on a moderately loaded heterogeneous system, and corresponding result is shown in Figure 5.8. Here, fmaxHP = 1.0, fmaxLP = 0.8 and U = 0.6 (60% load on power-efficient LP core). The value of n is set to 10. With reference to the energy consumption of SlowerP at k = 5, we normalize the obtained results. It may be ob- served from Figure 5.8 that the energy consumption of FENA-SCHED increases when the number of faults in the system increases whereasSlowerP exhibits a constant energy consumption. This is because the amount of backup slots reserved on the HP core by FENA-SCHED increases with an increase in k. This reduces the idle times on HP processing core and results in higher energy consumption. On the other hand, SlowerP is oblivious to the number of faults to be tolerated and hence, it assigns backup slots for all primary tasks onLP core toHP core, results in a constant energy consumption.
It may also be observed that even though the energy consumption of FENA-SCHED increases with k, it always performs better than SlowerP. This is due to the fact that FENA-SCHED reserves only a fixed amount of backup slots on HP core with respect to the number of faults to be tolerated and allows BB overloading within these backup slots. For example, when k = 2, FENA-SCHED and SlowerP yield an overall energy consumption of 70mJ and 110.21mJ, respectively. When k increases to 5, the over-
all energy consumption of FENA-SCHED becomes 87.98mJ, whereas that of SlowerP remains same as 110.21mJ.
0 20 40 60 80 100 120
1 2 3 4 5
Normalized energy consumption (%)
Number of faults
FENA-SCHED SlowerP
Figure 5.8: Impact of number of faults
Experiment 3- Impact of deadline: Figure 5.9 shows the impact of deadline on a heterogeneous system with fmaxHP = 1.0,fmaxLP = 0.8 and U = 0.6. The values of n and k are set to 10 and 4, respectively. With reference to the energy consumption ofSlowerP at D= 200, we normalize the obtained results. It may be observed from Figure 5.9 that as expected, the energy consumption of both FENA-SCHED and SlowerP increases when deadline increases from 100ms to 200ms. This is because, for a given number of tasks and total utilization, task execution time increases with an increase in the deadline, leading to higher energy consumption. For example, when D= 100ms,FENA-SCHED andSlowerP yield an overall energy consumption of 41.24mJ and 55.13mJ, respectively.
When deadline increases to 200ms, their overall energy consumptions become 82.41mJ and 110.21mJ, respectively.
Experiment 4- Impact of the maximum speed of the LP core: In this experi- ment, we have varied the maximum speed of the LP core (fmaxLP ) on a moderately loaded heterogeneous system. Figure 5.10 depicts the effect of fmaxLP on normalized energy con- sumption. Here, fmaxHP = 1.0, n = 10 and U = 0.6. The value of k is set to 4. With reference to the energy consumption of SlowerP at fmaxLP = 0.9, we normalize the ob-
5.3 Experiments and Results
0 20 40 60 80 100 120
100 125 150 175 200
Normalized energy consumption (%)
Deadline (ms)
FENA-SCHED SlowerP
Figure 5.9: Impact of deadline
tained results. It may be observed from Figure 5.10 that for a fixed number of tasks and total utilization, the energy consumption of bothFENA-SCHED andSlowerP increases when fmaxLP increases. This is because the power consumption characteristics of the LP core heavily depends on its operating frequency. Therefore, the power consumption of LP core increases asfmaxLP increases and results in higher energy consumption. For exam- ple, whenfmaxLP = 0.6,FENA-SCHED and SlowerP yield an overall energy consumption of 75.52mJ and 96.37mJ, respectively. WhenfmaxLP increases to 0.9, their overall energy consumptions become 85.86mJ and 117.13mJ, respectively.
0 20 40 60 80 100 120
0.6 0.65 0.7 0.75 0.8 0.85 0.9
Normalized energy consumption (%)
Maximum speed of the LP core FENA-SCHED
SlowerP
Figure 5.10: Impact of the maximum speed of the LP core
Experiment 5- Impact of number of tasks: Here, the number of tasks (n) on a moderately loaded heterogeneous system has been varied. Figure 5.11 depicts the effect of n on normalized energy consumption. Here, fmaxHP = 1.0, fmaxLP = 0.8 and U = 0.6.
The value of k is set to 4. With reference to the energy consumption of SlowerP at n = 30, we normalize the obtained results. It may be observed from Figure 5.11 that the energy consumption of FENA-SCHED decreases when the number of tasks in the system increases, whereasSlowerP exhibits a constant energy consumption. This is due to the fact that for a fixed workload, the average individual task utilization decreases with an increase in the number of tasks and hence, task execution time decreases. This reduces the length of backup slots reserved on the HP core by FENA-SCHED (for handling k faults), leads to lower energy consumption. On the other hand, SlowerP is oblivious to the number of faults to be tolerated and hence, it assigns backup slots for all primary tasks on the LP core, to HP core, and this results in constant energy consumption. For example, when n= 10, FENA-SCHED and SlowerP yield an overall energy consumption of 82.41mJ and 110.21mJ, respectively. When n increases to 30, the overall energy consumption of FENA-SCHED becomes 66.12mJ, whereas that of SlowerP remains same as 110.21mJ.
0 20 40 60 80 100 120
10 15 20 25 30
Normalized energy consumption (%)
Number of tasks
FENA-SCHED SlowerP
Figure 5.11: Impact of number of tasks