# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Experimental Environment Setting  
During simulation execution, a job randomly arrives every \(\mathrm{clk_{inj}}\) clock signal in the job queue to simulate the dynamic task environment. \(\mathrm{clk_{inj}}\)'s expectations are scale, \(\mathrm{scale}=\mathrm{E}\left(\mathrm{clk_{inj}}\right)\). The inter-arrival time of the \(\mathrm{clk_{inj}}\) follows the exponential distribution, \(\mathrm{clk_{inj}}\sim\mathrm{exponential}\Big{(}\frac{1}{\mathrm{scale}}\Big{)}\).  
Assuming the multifunction RF system has the ability to handle different RF applications, real-world application configurations are more complex than classic application configurations. Table 1 shows the classic DAG comprising of 10 randomly generated tasks. WiFi-TX, WiFi-RX, Single-carrier Tx (SCT), Single-carrier Rx (SCR), Range Detection, and Temporal Mitigation are the six common RF applications. Their corresponding hardware resource profiles are obtained from engineering data. Figure 5a displays the respective DAGs for WiFi-TX [31]. Here, Inverse Fast Fourier Transform (FFT) tasks are computationally intensive, taking 10 times longer to process on a CPU than on a HW accelerator. Figure 5b shows the computational execution time of each task on the corresponding PE that we obtained from statistics in the actual project; in Figure 5a, the value inside the circle represents the task ID, and the value beside the connecting line represents the data volume between the WiFi-Tx tasks. The communication bandwidth between PEs in the hardware configuration file is set as follows: the bandwidth value between HW Acc2 and the other PEs (cpu A15, cpu A15 and HW Acc1) is set to 10, and the bandwidth value be-tween all other PEs is set to 1000. The data volume between tasks and the communication bandwidth between PEs are known, and the communication delay can be derived based on Equation (2). Based on the above profiles in the simulation platform, MASA determines which PE to assign to the current highest priority task in the task-mapping phase based on the constraints of equations such as EST and EFT. Network structure and training parameters are listed in Table 2.  
These applications show different workload characteristics due to differences in DAG structure and computational requirements. For instance, WiFi-TX demonstrates a considerable level of parallelism compared to SCT and SCR, which contain sequential links among nodes, thus consuming less energy during low bandwidth conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application Type** & **Application** & **Number of Tasks** & **Max Width, Depth** \\ \hline Real-world’ & WiFi-TX & 27 & 5, 7 \\ Communication & WiFi-RX & 34 & 5, 10 \\ Application & Single-carrier Tx & 10 & 1, 10 \\  & Single-carrier Rx & 10 & 1, 10 \\ \hline Real-world’ Radar & range detection & 7 & 2, 6 \\ Application & Temporal Mitigation & 10 & 2, 6 \\ \hline \multicolumn{3}{c}{Classic DAG} & 10 & random \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark of applications.  
For the purpose of comparison, two types of representative scheduling algorithms have been considered: The heuristic algorithms MET [32] and HEFT [17], and the neural scheduling algorithms SCARL [33], decima [23], and SoCRATES [22]. This paper incorporates the mainstream homogeneous resource-scheduling algorithm decima into the comparative experiments to emphasize the necessity of designing heterogeneous scheduling algorithms. Decima is dedicated to resource scheduling in homogeneous environments, and SCARL only supports DAG for link structure, so its configuration must be modified in simulation slightly, and each experiment is repeated five times.