# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 5 Experimental Results
### Performance Tests  
The testbed described makes it possible to verify the behavior of the ME system and i-EVS under stress conditions. To this end, qualitative tests have also been performed during real-life crowded events in which the ME system and i-EVS have been demonstrated. For example, during a crowded event (the 2018 Mobile Word Congress), we found that uploading a file of a size equal to 100 MB at peak hours can take times of the order of tens of minutes, due to congestion in the backhaul connection to the public Internet. Conversely, the same upload to the local i-EVS eSTORE storage system, accessed through the same radio network, took about 20 s.  
A key aspect for a commercial testbed is the computing performance it can achieve. Thus, in the testbed, many loading tests were performed on i-EVS. In particular, the performance of the VTU VNFC has been thoroughly characterized and compared to the one typically achievable in a standard data center using only x86 architectures without HW acceleration. An example of the performed tests is summarized in Table 1. In this test, parallel transcoding sessions were launched, using video sequences (stored in the local eSTORE storage system) as inputs. The VTU input files were in high-definition format (1920 \(\times\) 1080 pixel), compressed according to the H.264 coding scheme. For each input video sequence, the VTU VNF provides, as output, four video sequences at different resolutions--namely VGA (480 \(\times\) 640 pixel), HD480 (480 \(\times\) 852 pixel), HD720 (720 \(\times\) 1280 pixel), and HD1080 (1080 \(\times\) 1920 pixel)--so that users can access video contents at the resolution that best fits their devices. The average time required by the parallel sessions to complete the transcoding process was accurately measured by monitoring the VTU running instance, and by temporally (and artificially) synchronizing the sessions. Since the number of frames of the input video sequences was known a priori, the average transcoding performance expressed in terms of processed frames per second (fps) was easily obtained. The following table presents some significant results that show the different performance achieved with and without GPU acceleration (with all the available virtual cores of the GOMA server assigned to the VTU). The first column of Table 1 reports the number of concurrent sessions; the second and third columns illustrate the performance in fps with and without GPU, respectively, while the last column shows the acceleration factor achieved, expressed as the ratio of the achieved performance with and without GPU. The superiority of the GPU-accelerated solution is clear, and is even confirmed when considering energy efficiency, as shown by the experiment summarized in Table 2. In this case, the basic measurement setup was the same as Table 1. Additionally, the power consumption of the GOMA server hosting i-EVS was monitored. To this end, power measurements were carried out in DC, testing the current flowing on the reference voltages of the GOMA server motherboard (12 V, 5 V, 3.3 V). A current clump was used, with a resolution of 0.1 A, resulting in a maximum uncertainty of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transcoding** & **SW-Only Transcoding** & **GPU-Accelerated Transcoding** & **Performance** \\
**Sessions** & **Performance (fps)** & **Performance (fps)** & **Improvement Using GPU** \\ \hline
1 & 60.2 & 205 & 3.4\(\times\) \\
2 & 32.6 & 186.2 & 5.8\(\times\) \\
4 & 16.3 & 97.8 & 6\(\times\) \\
8 & 8.7 & 49.5 & 5.7\(\times\) \\
16 & 4.3 & 24.75 & 5.7\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transcoding performance of i-EVS with and without GPU. 1.2 W (on 12 V voltage rail). Table 2 gives the observed transcoding performance expressed in fps, normalized to the measured power consumption in Watt, thus obtaining the efficiency index expressed in fps/Watt reported in the fourth column.