# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Algorithm Performance Analysis
#### 5.2.1 MASA in Classic DAG Environment  
In Figure 6, we show the performance results of different scheduling algorithms under the classical DAG environment configuration. We also plot the algorithms' performance results under different numbers of applications, respectively, as in Figure 6a-f. As the arrival rate of applications increases, the average application execution time of each algorithm gradually increases. However, the performance of the MASA algorithm proposed in this paper consistently outperforms the other algorithms. The graph shown in Figure 6a represents the execution results of a classical DAG with successive arrivals to the scheduler, under the condition of following an exponential distribution. To comprehend the performance of the scheduling algorithm, an analysis is conducted on the average application execution time at \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Job queue capacity & 3 & Optimizer & Adam \\ Node input dimensions & 6 & Scale (job inject interval’ expectation) & 20/40/60/80/100 \\ Output dimensions & 8 & CLK (simulation length) & 10,000 \\ Maximum depth & 3 & T\({}_{\text{mean}}\) (episode length’ expectation) & 10,000 \\ Hidden dimensions & [16; 8] & M\({}_{\text{max}}\) (number of Agent) & 1/2/4/6/8/10 \\ \(\beta\) (learning rate) & 0.0001 & \(\eta\) (entropy coefficient) & 0.1 \\ \(\alpha\) (learning rate) & 0.0003 & y (discount factor) & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm parameter setting. Figure 5: (**a**) WIFI-Tx’ DAG; (**b**) WIFI-Tx task execution time on different PEs profiles. different scales. At a low arrival rate (scale = 100), the heuristic-based algorithm's scheduling performance is clearly weaker than the neural network-based algorithm's scheduling performance. When the arrival rate increases (scale = 20), the MASA algorithm proposed in this paper also outperforms the other neural scheduling algorithms, with an improvement of about 9% over the performance of SCRAL and about 18% over the performance of decima and SoCRATES. Figure 6f illustrates an application pool of 10 classical DAGs, each with the same arrival probability. As shown by the figure, the algorithm presented in this paper offers superior performance compared to other algorithms in all application arrival rates, while also improving with an increase in the number of applications. Figure 6: (**a**) Results of scheduling algorithms in different numbers of classical DAGs: (**a**) the DAG pool consists of one classical DAG; (**b**) the DAG pool consists of 2 random classical DAGs; (**c**) 4; (**d**) 6; (**e**) 8; (**f**) 10. Figure 7: Scheduling results of scheduling algorithms for different real RF applications (**a**) WiFi-TX; (**b**) WiFi-RX; (**c**) Single-carrier Tx; (**d**) Single-carrier Rx; (**e**) Ranging detection; (**f**) Temporal mitigation. The data presented in Figure 6 show that the heuristic algorithm can yield results comparable to other algorithms when the number of tasks is small and the state environment is relatively uncomplicated. However, as the task injection rate increases, the complexity of the state space faced by the scheduler based on the heuristic algorithm increases dramatically, and the heuristic algorithm is unable to solve the situation very well. Decima is based on a graph neural network for encoding the state space. Compared to the heuristic algorithm, it can provide a more detailed explanation of the complex state space, leading to significantly improved performance, especially when dealing with large task injection rate, but it should be noted that Decima is mainly proposed to solve the isomorphic resource-scheduling problem. Based on this foundation, SCARL and SoCRATES introduce the fundamental concept of RL to improve the results when the model is trained on the dynamically transformed heterogeneous resource platform through the interaction between the environment and the Agent, and the experimental results also prove the effectiveness of RL in the problem setting.