# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.1 Average Packet Delay  
Figure 4 presents the comparison between the average packet delay and buffer size of different methods in scenario 1. We can find that under the same buffer size, our proposal performs better than ProPull and LPBMMS in terms of average packet delay. When the buffer size is 500, compared with ProPull and LPBMMS, BDMM reduces the average packet delay by 25.8% and 15.98%, respectively. This is because BDMM uses Mobile Path Notification to optimize the handover path and converge the switching path to Content Server- \(\rightarrow\) R3 \(\rightarrow\) R4 \(\rightarrow\) UPF2 \(\rightarrow\) BS3 \(\rightarrow\) UE. ProPull causes the switching path to converge to Content Server \(\rightarrow\) R3 \(\rightarrow\) R2 \(\rightarrow\) R1 \(\rightarrow\) R4 \(\rightarrow\) UPF2 \(\rightarrow\) BS3 \(\rightarrow\) UE by sending PI to the previous access router (R1). LPBMMS uses INTEREST_RED to update the router forwarding table between the source base station and the predicted target base station. If the prediction is successful, the handover path converges to Content Server \(\rightarrow\) R3 \(\rightarrow\) R2 \(\rightarrow\) R1 \(\rightarrow\) R4 \(\rightarrow\) UPF2 \(\rightarrow\) BS3 \(\rightarrow\) UE. If the prediction fails, LPBMMS broadcasts its new name prefix to make the switching path converge to the shortest path (Content Server \(\rightarrow\) R3 \(\rightarrow\) R4 \(\rightarrow\) UPF2 \(\rightarrow\) BS3 \(\rightarrow\) UE). Therefore, when the prediction success probability is 50%, the average packet delay of LPBMMS is better than ProPull, but worse than BDMM. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mobile data flow rate \((R)\) & [5; 10; 15] Mbit/s \\ Average packet size \((P)\) & 1000 bytes \\ Number of mobile data flow \((N)\) & [1; 10] \\ Mobile buffer size \((B)\) & 100–900 packets \\ Mobile data flow prioritization \((\mathrm{w})\) & [0.95, 0.05] \\ Threshold update period \((\Delta t)\) & 20 ms \\ Time interval UE disconnection and reconnection from S-BS to T-BS \((T_{ST})\) & [80, 100] ms \\ Response latency between NRS and ARs \((T_{RA})\) & 1 ms \\ Wired link delay \((T_{\mathit{virtual}})\) & 5 ms \\ Wireless link delay \((T_{\mathit{virtualess}})\) & 5 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment parameters and values. Figure 4b presents the comparison between the average packet delay and buffer size of different methods in scenario 2. We can find that under the same buffer size, our proposal performs better than ProPull and LPBMMS in terms of average packet delay. However, the average packet delays of these three methods are similar. This is because the average packet delay is mainly determined by the switching path. In the non-cross-UPF handover scenario, the switching paths of BDMM, ProPull and LPBMMS are Content Server \(\rightarrow\) R3 \(\rightarrow\) R2 \(\rightarrow\) R1 \(\rightarrow\) UPF1 \(\rightarrow\)BS1 \(\rightarrow\) UE. The small gap between BDMM and ProPull is caused by the transmission delay of buffered packets. In BDMM, after the source base station senses that the UE handover is completed, the source base station sends a Mobile Event Message to trigger the release of buffered data. ProPull needs to wait for the notification issued after the UE handover is completed. Therefore, the transmission delay of BDMM buffered packets is slightly lower than that of ProPull. When LPBMMS fails to predict, the forwarding path of the source base station buffer packet is BS2 \(\rightarrow\) R1 \(\rightarrow\) BS1 \(\rightarrow\) UE. Therefore, when the prediction success probability of LPBMMS is 50%, the average packet delay performance of LPBMMS is the worst. In summary, in the cross-UPF handover scenario, BDMM optimizes the switching path and reduces the average packet delay. Compared with ProPull and LPBMMS, BDMM reduces the average packet delay by approximately 25.8% and 15.98%, respectively.