# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 5 Performance Evaluation  
We carried out experimental simulations to analyze the performance of our proposed scheme. We first introduce the design and evaluation objectives of the simulations. We compare it with existing scheduling algorithms designed in similar environments. The overdue time under high load is greater than 1. According to the reward function (11), it can be seen that the reward is negative at this time. In the early stage, due to the fluctuation of the weights of exploration and early learning, the rate of return of the learning strategy decreases. With the increase in the number of learning samples, the model tends to be stable, and returns gradually increase. After the model converges, it can be seen that the algorithm in this paper has obvious advantages in cost and can minimize the overdue time. Since the Min-Min algorithm transfers the task to the VM that can complete it soonest, its throughput is dominant, and it can achieve excellent results in both timeout period and makespan. However, due to the lack of utilization of low-performance VMs, its cost is relatively high, and under high load, the timeout period and makespan cannot be optimized. Under high load, it can be clearly seen that task assignment to VMs has a greater impact on subsequent assignments, which reduces the fault tolerance rate. Therefore, the simple distribution methods of random and round robin perform poorly under high load. Low load: By modifying the arrival rate to 0.05, the average overdue times of the comparison algorithms, except for the random algorithm, are 0. At this time, according to the reward function (11), the main source of reward is no longer overdue time, but cost and throughput. Three weight ratios are set to verify the function of the weight and performance of the algorithm under low load, with weights of 0.9-0.1, 0.5-0.5, and 0.1-0.9. The experimental results are shown in Figure 5.  
Figure 4: Performance of SAL-DQTS algorithm and comparison algorithms under high-load environment. Figure 5: Performance of SAL-DQTS algorithm with different weights and comparison algorithm in a low-load environment. As shown in Figure 5, in a low-load environment, the overdue time can easily converge to a target range of 0 or close to 0. According to the setting of the reward function, when the overdue time is less than 1, the proportion of the return comes from throughput, cost and overdue time, and the closer to 0, the larger the proportion of the first two items. SAL-DQTS9-1 takes cost as the main reward, so the use of more low-performance machines leads to low throughput and overdue tasks. Since the overdue time is close to zero, the penalty value is too small. The return from cost reduction can offset the corresponding penalty. The remaining two items tend to be balanced and have high throughput, so their overdue time converges to 0. From the comparison of the target curves of SAL-DQTS9-1 and SAL-DQTS1-9, the weight obviously has a certain influence on the effects of different indicators. A comparison with SAL-DQTS5-5 shows that in terms of cost, the convergence trend tends to the middle, while the throughput performance is similar to that of SAL-DQTS1-9. This is because the relationship between throughput and cost is not simple. After all, the cost is also related to time. Due to marginal effects, when the throughput is close to the maximum value, the throughput increase caused by the equal cost keeps dropping. At this time, the setting of the weight can make the optimization direction biased to the side with the higher weight. Moreover, the correction parameter \(\alpha_{r}^{1}\), \(\alpha_{r}^{2}\) in the reward functions (12) and (13) is set according to their respective maximum returns, so these two parameters constitute the upper limit of the return values under a single target similar. Moreover, the reward function curves of the two are not the same, so the weight cannot strictly make the optimization result and the weight ratio consistent.