# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Algorithm Performance Analysis
#### 5.2.2 MASA in Real-World Environment  
To further illustrate the superiority of our proposed algorithms, we test the performance of each algorithm in a job profile in a real mission scenario, and the experimental results are shown in Figures 7a-f and 8. The configuration for WiFi-RX job is the most complex, consisting of 34 tasks with complicated dependencies. However, its application execution time is approximately three times longer than that of the latter. The WiFi-TX job includes 27 tasks, which is more than a typical task configuration environment. However, its execution time does not increase much compared to a classical environment because the dependencies between its tasks are mostly linear, and it can be observed that MASA outperforms the other compared algorithms in every application arrival rate (scale). The configuration of WiFi-RX is the most complicated, which contains the largest number of tasks and complex dependencies between tasks, so the execution time of this task is much larger than all the other application configurations in this paper. It can be seen that the algorithm proposed in this paper, MASA, has a significant improvement over the other heuristics and neural scheduling algorithms, which is mainly due to the fact that MASA adopts the attention mechanism, which can effectively analyze the topology between complex tasks to improve the performance of the neural scheduling network. The SCT task configuration includes only 8 tasks and straightforward dependencies, so its execution time is relatively low, and when the task injection rate is low, there is not much difference in the performance of the neural scheduling algorithms (better than the heuristic algorithms), but when the task injection rate reaches the maximum, the performance of  
Figure 8: Algorithm performance results in mixed real-world environments (consisting of 6 real-world applications with the same probability). the neural scheduling algorithms is better than the heuristic algorithms. Nevertheless, when the task injection rate is maximum, MASA's algorithm surpasses the performance of other comparable algorithms. The task environment configuration of SCR is similar to that of SCR, except it demands a higher data volume between tasks. According to the experimental results, both SoCRATES and MASA's scheduling schemes yield the lowest task execution time, and MASA slightly outperforms SoCRATES. The configurations of the range detection and Temporal Mitigation environments show that MASA performs better than the other algorithms at all task arrival rates. In conclusion, the study demonstrates that the simulation environment is scalable by validating the proposed algorithm under different real profiles. This paper includes a simulation of six applications (WiFi-TX/RX, SCT, SCR, range detection, and Temporal Mitigation) concurrently running in a real environment to analyze algorithm performance under varying task arrival rates. Figure 8 shows that the heuristic algorithm does not dominate the overall task scheduling due to the extended execution time of the WiFi-TX. The complexity of the task environment results in higher demands on the performance of the scheduling algorithms as the task injection rate increases, and the MASA algorithm gradually demonstrates its dynamic scheduling capability in complex task environments. The real-world job profiles are more complex, so the scheduler's ability to encode the state space puts forward higher requirements, and its final average task execution time depends largely on each scheduler's ability to interpret the state space. MASA's excellent dynamic scheduling ability is due to the fact that the introduced attention mechanism, which eliminates the need to adjust the _scale_ of the network model based on the specific task environment and provides a better interpretation of the dynamic environment. Figure 9 illustrates the convergence process of actor network during training. It can be observed that the actor-critic strategy results in convergence around 100 episodes.