## 3 Results  
In this section, we describe the obtained service latency results for the three evaluated scenarios of on-device, server-based, and service function split object detection service with YOLOv2 as described in prior sections. We initially present our overarching results in Table 2.  
Figure 6: Image-based compression methods for JPEG input assumption, from [29]. We first observe that for the two scenarios of fully on-device (\(\alpha=1\)) and fully on-server (Store), the server-side processing incurs a delay of just over 2 seconds. For the client-only service latency, we notice an exponential increase as the performance of the client in relation to the server diminishes. At \(\alpha=0.2\), the client requires almost a 12-fold increase to process the image. As outlined in the motivation in Section 2.3, we employ this as a comparison point to the server for the compute-and-forward scenario. The compute-and-forward case provides a total service latency that is just below that of the client having the full server resources itself. We additionally notice from the table that the median and average are fairly close to another, with generally less than one percent difference. A visual comparison of these three service approaches is illustrated in Figure 7.  
We observe that the store-and-forward approach is in this comparison not desirable at all, as it exhibits the highest service latency. The comparison of an assumed full server-level CPU performance on the client side with the compute-and-forward approach with only 20% server-side equivalent resources on the client side showcases a significant overlap in service time distributions. Particularly, we notice that 50% of the compute-and-forward latency times observed are lower than any local processing, while the remaining 50% are spread over the entire client-side processing range. In comparison, the store-and-forward \begin{table}
\begin{tabular}{l r r r r r r r r r r r} \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline Min & 116.498 & 74.990 & 50.103 & 28.381 & 16.132 & 11.883 & 9.517 & 8.229 & 7.368 & 6.575 & 8.937 & 6.012 \\ Median & 150.701 & 81.901 & 54.140 & 31.276 & 16.693 & 12.359 & 9.984 & 8.735 & 7.678 & 6.882 & 9.170 & 6.581 \\ Average & 147.884 & 81.656 & 53.902 & 31.275 & 16.746 & 12.406 & 9.964 & 8.695 & 7.712 & 6.904 & 9.242 & 6.643 \\ Max & 155.296 & 85.397 & 57.722 & 34.629 & 17.996 & 13.049 & 10.623 & 9.319 & 8.228 & 7.370 & 9.772 & 7.496 \\ StdDev & 8.952 & 2.565 & 1.844 & 1.639 & 0.450 & 0.320 & 0.313 & 0.296 & 0.253 & 0.226 & 0.237 & 0.408 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of obtained service latency \(T\) results for YOLOv2 performed on-device (with varying degrees \(\alpha\) of server computation resource), store-and-forward networking with server-side processing, and compute-and-forward with \(\alpha=0.2\) client-side processing up to layer 8 of YOLOv2 and remainder processing server-side. All results are in seconds. Figure 7: Service latency likelihood for YOLOv2 performed on-device only (with device computational resources equal to server-side resources, \(\alpha=1\)), store-and-forward networking with server-side processing only, and compute-and-forward with \(\alpha=0.2\) client-side processing up to layer 8 of YOLOv2 and remainder processing server-side. approach yields a lower spread of latency values and is more comparable to the on-client processing in this regard. We now consider the impact of different local processing capabilities of the client in comparison to the server.