## 4 AOS Elastic Slot Scheduling Strategy
03: if (\(R1==1\)&& \(w_{t1\_ava}==0\)) then
04: if (\(w_{t1\_used}<W_{t1\_min}\)) then
05: Synchronous services preempt asynchronous non-real-time services' time slots, calculated according to Formula (18) (\(i=1,\ j=3\)). 06: else if (\(w_{t3\_used}<W_{t3\_min}\)) then
07: if (\(R2==0\)&& \(R3==0\)) then
08: Synchronous services preempt asynchronous real-time services' time slots, calculated according to formula (18) (\(i=1,\ j=2\)). 09: end if
101: else then
11: if (\(R2==0\)) then
12: Synchronous services preempt asynchronous real-time services' time slots, calculated according to Formula (18) (\(i=1,\ j=2\)). 13: end if
14: end if
15: else if (\(R2==1\)&& \(w_{t2\_area}==0\)) then
16: if (\(w_{t1\_area}>w_{t3\_area}\)) then
17: Asynchronous real-time services preempt synchronous services' time slots, calculated according to Formula (18) (\(i=2,\ j=1\)). 18: else then
19: Asynchronous real-time services preempt asynchronous non-real-time services' time slots, time
```  
**Algorithm 2** Elastic Time Slot Scheduling Algorithm  
```
01: Input: For the three types of traffic, the number of available time slots \(w_{t1\_area}\), \(w_{t2\_area}\), \(w_{t3\_area}\), the number of used time slots \(w_{t1\_used}\), \(w_{t2\_used}\), \(w_{t3\_used}\), and the transmission request states \(R1,R2,R3\). 02: Output: The total number of time slots dynamically allocated for three types of traffic \(w_{t1},w_{t2},w_{t3}\). 03:



## 5 Simulation and Analysis

### Simulation Parameters and Algorithms

The simulation parameters are set as follows:

1. Simulation time \(T=300\) s;
2. Processing speed ranges from 0.1 \(\times\) 10\({}^{3}\) to 1.6 \(\times\) 10\({}^{3}\) frames/s, covering the data source speed range;
3. The average traffic prediction accuracy is 10%;
4. Three types of data sources are simulated: synchronous traffic, asynchronous real-time traffic, and asynchronous non-real-time traffic, with a total of 10 VCs. The detailed data source settings are shown in Table 1.

When setting up the data source, two working conditions were selected based on the spacecraft's traffic fluctuation, including the normal condition and the stressed condition. In the normal condition, synchronous traffic uses uniform distribution, asynchronous real-time traffic uses Poisson distribution, and asynchronous non-real-time traffic uses uniform distribution, with the average variance of overall traffic fluctuation being 0.428. In the stressed condition, synchronous traffic uses Poisson distribution, asynchronous real-time traffic uses actual terrestrial internet traffic datasets, and asynchronous non-real-time traffic uses Poisson distribution, with the average variance of overall traffic fluctuation being 4.76. The actual dataset used is from the MAWI Working Group Traffic Archive [27], which primarily originates from actual internet traffic sampling points on the WIDE backbone network, and its traffic fluctuation characteristics are inevitably more complex than those of spacecraft network traffic. We use a 15-min-long packet trace taken on 18 October 2024, at 14:00 JST, containing about 248 million IPv4 packets. Four types of actual traffic data were selected, and the data were scaled proportionally to match the average rate of the data to be simulated, with the scaled traffic data serving as the data source. In this simulation, the following three algorithms are selected for comparison: the traditional AOS multiplexing algorithm (TAMA), the fixed-time-slot-based AOS integrated multiplexing algorithm (FAMA), and the elastic-time-slot-based efficient AOS integrated multiplexing algorithm (EAMA). The detailed configurations of these three algorithms are presented in Table 2.