# Networking for Cloud Robotics: The DewROS Platform and Its Application
## 6 Experimental Evaluation
### Analysis of Response Time
It is worth noting that the API available for interacting with CVI--through the function annotate_video(features, video_context, input_content)--performs all the actions (upload, if needed, and annotation request/response) in a single call. The call requires information about the video file that will be analyzed, its location, what type of analysis is requested, etc. To isolate the video upload contribution, we used the API for the file upload in a previously created bucket on GCS--through the function upload_from_filename(source_file_name)--and measured the time required for this task. Figure 13 reports such a time for the satellite Internet connection. The figure evidences the high times needed with this connection and the clear dependence on the file size. The upload time with a video of size 24 MB is about 73 s, which implies a throughput of about 2.5 Mbps. This throughput value is smaller than the one measured with speedtest-cli (reported in Table 3), but relates to a real case study and is, therefore, much more realistic for real-world application. Comparing these results with the ones from Figure 12, it can be noticed that the upload time can comprise about 85% of the total average response time for this video (88 s) reported in Figure 12.  
We next compare this with the fastest Internet connection. Figure 14 shows the upload times of the UNI network connection. For the UNI network, the upload time with a video of size 24 MB is about 3.2 s, which implies a throughput of about 60 Mbps towards the Google server in the Cloud. Comparing these values with the ones from Figure 12, it is possible to see that the upload time for the UNI network is only 20% of the response time for this video (about 15 s). Figure 12: Response time using the UNI, HOME and SAT network connections, in case of locally hosted video files, with different sizes (small, medium and large). The other aspect that calls for further investigation is the variability observed in the boxes in Figure 14. We, therefore, examined the coefficient of variation (\(CoV\)), calculated as the ratio of the sample standard deviation to the sample mean. The \(CoV\) is reported in Figure 15. A trend can be observed related to the two variables of video size and Internet connection. In particular, the larger the video file size and lower the upload speed of the specific connection, the lower the coefficient of variation and, therefore, the dispersion in that sample. Further analyses revealed that the difference between these values is mainly because the variation in the response time for different requests is almost constant and independent of the mean, where values are, instead, very different, as a function of the several variables considered. The time taken to upload the video on the Cloud can reach about 85% of the total CVI response time for the satellite and up to 20% for the UNI. The remaining part of the response time, the one not related to video upload, was then analyzed to understand how it is dependent on the variables under control. The same API (through the function annotate_video(features, video_context, input_content)) was used, but with videos which were already loaded onto the Cloud. Figure 16 reports the obtained results. In this case, the difference between the distributions is smaller than in the previous case and the difference between the medians in every connection is in the range of 5 s. Here, it can be observed that large- and medium-sized videos need more time for analysis with respect to small ones, with all the Internet connections. This is different from what occurred with video length and implies that CVI response time depends much more on the volume of bytes than on the number of frames to be analyzed. The higher response times obtained by SAT with all the videos was less expected. Figure 14: Upload Time Distributions for each video type using the University (UNI) Internet connection.