# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 5 Performance Evaluation
However, it is possible to modify the weight ratio to show the optimization tendency of a certain target. Through comparison, it can be seen that more balanced performance can be obtained in all aspects with a weight of 0.5-0.5. The advantage of the SAL-DQTS algorithm under low load is not obvious except in cost optimization. Min-Min will still adopt the earliest completion strategy under low load, so it will still tend to use high-performance VMs. Although the expiration time, throughput, and makespan are optimized, the cost increases accordingly. Under low load, there is no need to pursue such a high throughput rate and makespan. The appropriate sacrifice of some performance can still meet the demand and decrease the cost. Therefore, in the comparison function under low load, round robin has better overall performance. Medium load: We modify the arrival rate to 0.2. At this time, the task overdue time under the comparison function scheduling is between 0 and 1, and the task load intensity is medium. Since the overdue time is less than one, according to the reward function (11), it can be seen that the return is affected by the trio of \(r^{1}\), \(r^{2}\), and \(r^{3}\), and as the overdue gets closer to one the first two items become smaller. It can be seen from the low-load experiment that the weights of \(r^{1}\) and \(r^{2}\) are set to 0.5 and 0.5, respectively, so it is recommended to set this ratio in the medium-load experiment. As shown in Figure 6, the overdue time of SAL-DQTS under moderate load and makespan is close to the optimal comparison algorithm. Although it is not as successful as Min-Min and round robin in throughput, it has obvious advantages in cost. Because it is a medium load, Min-Min has consistent performance with RR on overdue time and makespan, but because it prefers high-performance VMs to pursue higher throughput rates, the cost is higher than with RR. Under medium load, we believe that the performance supply is comparable to the task demand, and a small part of the response time and the makespan's lower cost of zone change can be sacrificed within a certain range to achieve greater benefits. Based on this implementation, the comprehensive performance of RR is better than that of Min-Min. Through the above three experiments, it can be seen that the proposed algorithm can dynamically adapt to changes of load in the environment and learn the corresponding strategies, meet user requirements as much as possible in the dynamic cloud environment, achieve excellent overdue time and makespan, and realize obvious advantages in task cost. Complexity: Table 3 shows the relevant indicators of the complexity of the algorithm, including the flops [38], memory, time complexity, and simulation execution time. Due to the model structure, the calculation amount is proportional to the number of VMs, which is \(N*247\)kFlops, where \(N\) is the number of VMs. This process is parallel, so the calculation time is not linear with the number of VMs, and the time complexity is \(247\) k. The Min-Min needs to select the smallest task and the earliest completed VM, so the complexity is \(N*M\), and \(M\) is the number of tasks. The Random and RR algorithms only use random and polling methods, respectively, so their complexity is 1. The last column in the table is the scheduling time of a single task of each comparison algorithm in the simulation environment. It can be seen that SLA_DQTS is significantly higher than the comparison algorithm. Although the training and decision making of the model can be asynchronous, compared with the traditional heuristic algorithm, deep reinforcement learning requires additional training costs and cannot be ignored.